Creating a Global Building Inventory for 

Earthquake Loss Assessment and Risk 

Management 

Reinforced 

concrete

31.9%

Steel framed

6.4% Others

0.3%

Traditional 

Wooden

32%

Wooden and 

fire-proofed 30%

Open-File Report 2008–1160 

U.S. Department of the Interior 

U.S. Geological Survey 
Creating a Global Building Inventory for 

Earthquake Loss Assessment and Risk 

Management 

By Kishor Jaiswal and David J. Wald 

Open-File Report 2008–1160

U.S. Department of the Interior

U.S. Geological Survey
U.S. Department of the Interior 

DIRK KEMPTHORNE, Secretary 

U.S. Geological Survey 

Mark D. Myers, Director 

U.S. Geological Survey, Reston, Virginia: 2008 

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Manuscript approved for publication, March 2008 

Edited by Lorna Carter 

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Suggested citation: 

Jaiswal, K.S., and Wald, D.J., 2008, Creating a global building inventory for earthquake 

loss assessment and risk management: U.S. Geological Survey Open-File Report 2008-1160,

103 p. 

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ii
Contents 

Contents.......................................................................................................................... iii

Figures.............................................................................................................................v

Tables .............................................................................................................................vi

Executive Summary .........................................................................................................1

Introduction ......................................................................................................................2

Inventory Literature and Data Review..............................................................................4

Inventory Data Sources ...................................................................................................7

UN Statistical Database on Global Housing (1993) .........................................................7

UN-HABITAT Database (2007)........................................................................................9

Housing Census Database (Country-Specific).................................................................9

World Housing Encyclopedia Database (Country-Specific) ...........................................10

Data Compiled from Published Literature ......................................................................11

Inventory Development ..................................................................................................11

Overview........................................................................................................................11

PAGER Specific Inventory Needs..................................................................................12

Methodology ..................................................................................................................12

Phase I: Database Identification, Preparation, and Confidence Rating..........................13

Data Identification ..........................................................................................................13

Geographic Location and Resolution .............................................................................13

Occupancy Classification...............................................................................................13

Construction Type Classification....................................................................................14

Average Occupancy by Construction Type....................................................................14

Attribute Rating ..............................................................................................................15

Phase II: Data Prioritization, Merging, and Country Assignments..................................15

Selection of Attribute Assignments Using Ratings and Vintage of Data Source ............15

Phase III: Data Development for Missing Entries and Synthesis ...................................16

Missing Entries and Country-Pairing..............................................................................16

PAGER Inventory Matrix Compilation ............................................................................17

Limitations of the Methodology ......................................................................................17

Illustration of Inventory Data Compilation for Peru.........................................................18

United Nations Housing Database.................................................................................18

UN-HABITAT (2007) DHS Database .............................................................................18

Population and Housing Census....................................................................................19

World Housing Encyclopedia (WHE) Reports................................................................19

Published Literature of Housing/Building Stock .............................................................19

Inventory Compilation for Selected Countries................................................................20

Australia.........................................................................................................................20

El Salvador.....................................................................................................................20

Indonesia .......................................................................................................................21

Iran.................................................................................................................................21

Italy ................................................................................................................................22

Japan .............................................................................................................................22

Mexico ...........................................................................................................................23

Nepal .............................................................................................................................23

New Zealand..................................................................................................................24

Taiwan ...........................................................................................................................24

The Philippines ..............................................................................................................25

iii
Turkey............................................................................................................................25

Application to the Continental United States..................................................................25

Web Delivery of Global Inventory...................................................................................26

Implementation Challenges............................................................................................27

Processing of Raw Data ................................................................................................27

Construction Type Mapping ...........................................................................................27

Occupancy by Construction Type ..................................................................................27

Confidence Rating .........................................................................................................28

Limitations of PAGER Inventory Database ....................................................................28

Housing Units and Building/Structure Type Inventory....................................................28

Wall Material and Structure Type...................................................................................28

Vintage of Database ......................................................................................................29

Resolution of Inventory Distribution ...............................................................................29

Scope of Inventory Updating..........................................................................................29

Summary........................................................................................................................30

Conclusions ...................................................................................................................31

Acknowledgments..........................................................................................................32

References Cited ...........................................................................................................33

Glossary.........................................................................................................................41

Appendix I......................................................................................................................42

Appendix II.....................................................................................................................45

Appendix III....................................................................................................................47

Appendix IV....................................................................................................................52

Appendix V.....................................................................................................................57

Appendix VI....................................................................................................................64

Appendix VII...................................................................................................................69

iv
Figures 

Figure 1. Histogram showing housing units by construction material for Hungary ......87

Figure 2. Schematic overview flowchart of the PAGER inventory development 

methodology ................................................................................................................88

Figure 3. Schematic flowchart of the PAGER inventory development methodology 

(Phase I – Population of databases and ratings) .........................................................89

Figure 4. Schematic flowchart of the PAGER Inventory Development Methodology 

(Phase II - Merging of Databases) ...............................................................................90

Figure 5. Map showing geographic coverage by country of the building inventory data 

from different sources aggregated in Phase II .............................................................91

Figure 6. Map showing the housing stock distribution pattern of Iran, identified as a 

possible representative building stock distribution for Afghanistan (where data is not 

available) based on recent vintage and rating of Iran over data from Pakistan............92

Figure 7. Map showing identification of most representative neighbor for Ecuador from 

all neighboring countries ..............................................................................................93

Figure 8. Map showing the building stock distribution in Armenia, used as a proxy for 

Azerbaijan based on data vintage................................................................................94

Figure 9. Schematic flowchart of the PAGER inventory development methodology 

(Phase III – Filling the missing elements) ....................................................................95

Figure 10. Photograph showing typical rural house in Bangladesh made of straw, mud, 

and timber frame..........................................................................................................96

Figure 11. Photograph showing general housing stock in Thailand (Bangkok) obtained 

from general Internet search........................................................................................97

Figure 12. Bar chart showing housing stock distribution for Peru (United Nations, 

1993)............................................................................................................................98

Figure 13. Photograph showing typical residential building stock in Peru constructed 

using brick masonry construction.................................................................................99

Figure 14. Pie chart showing distribution of housing stock in urban areas of Peru 

obtained from WHE-PAGER expert survey................................................................100

Figure 15. Pie chart showing distribution of housing stock in urban and rural areas of 

Iran obtained from housing census data (Iran Statistical Year Book, 2005)...............101

Figure 16. Photograph showing typical multistoried reinforced concrete apartment 

buildings with masonry infill walls in Ankara, Turkey..................................................102

Figure 17. Map showing global coverage of PAGER building inventory database with 

data compiled from various sources ..........................................................................103

v
Tables 

Table 1. Tabulation of the types of data sources, their parameters, assigned quality 

ratings, and their coverage ..........................................................................................70

Table 2. Housing units by construction material of external walls in the year 2004 for 

Tanzania from the UN (2007) database.......................................................................72

Table 3. List of countries, source of contact, and web links for countries with housing 

census data..................................................................................................................72

Table 4. (a) Census data compiled by the National Statistical Office of the Government 

of the Philippines for housing inventory by construction material, (b) Housing Census 

data showing distribution of housing units by construction material (wall and roof 

construction) for Pakistan.............................................................................................73

Table 5. List of countries with inventory data from published country-specific 

resources, articles, and reports....................................................................................74

Table 6. Residential construction type distribution for several Middle-Eastern countries, 

from Petrovski (1983)...................................................................................................75

Table 7. Housing stock distribution based on UN-HABITAT (2007) compiled for Peru76

Table 8. Housing stock distribution based on the 2000 Census of Population and 

Housing compiled for Australia ....................................................................................76

Table 9. Housing stock distribution based on the 1990 Census of Population and 

Housing compiled for El Salvador................................................................................77

Table 10. Housing stock distribution based on the Census of Population and Housing 

(2001) compiled for Indonesia......................................................................................77

Table 11. Housing stock distribution based on Ghafory-Ashtiany and Mousavi (2005) 

for the city of Bam, Iran................................................................................................78

Table 12. Distribution of residential building stock in Italy ...........................................78

Table 13. Distribution of residential building stock in Japan ........................................79

Table 14. Distribution of residential building stock in Mexico.......................................79

Table 15. Distribution of residential building stock in Nepal.........................................80

Table 16. Distribution of residential building stock in New Zealand.............................80

Table 17. Distribution of residential building stock in Taiwan.......................................81

Table 18. Distribution of residential building stock in Philippines.................................81

Table 19. Distribution of residential building stock in Turkey .......................................82

Table 20. Distribution of California building stock as obtained from the HAZUS (FEMA, 

2006) inventory database ............................................................................................83

Table 21. List of countries that provide inventory data obtained during WHE-PAGER 

survey and the name of the experts.............................................................................85

Table 22. WHE-PAGER survey response for Japan, contributed by Dr. Charles 

Scawthorn from Kyoto University, Japan .....................................................................86

vi
Creating a Global Building Inventory for

Earthquake Loss Assessment 

and Risk Management 

By Kishor Jaiswal1 and David J. Wald2

Executive Summary 

Earthquakes have claimed approximately 8 million lives over the last 2,000 years (Dunbar, 

Lockridge and others, 1992) and fatality rates are likely to continue to rise with increased 

population and urbanizations of global settlements especially in developing countries. More than 

75% of earthquake-related human casualties are caused by the collapse of buildings or structures 

(Coburn and Spence, 2002). It is disheartening to note that large fractions of the world’s population 

still reside in informal, poorly-constructed & non-engineered dwellings which have high 

susceptibility to collapse during earthquakes. Moreover, with increasing urbanization half of 

world’s population now lives in urban areas (United Nations, 2001), and half of these urban centers 

are located in earthquake-prone regions (Bilham, 2004). The poor performance of most building 

stocks during earthquakes remains a primary societal concern. However, despite this dark history 

and bleaker future trends, there are no comprehensive global building inventories of sufficient 

quality and coverage to adequately address and characterize future earthquake losses. Such an 

inventory is vital both for earthquake loss mitigation and for earthquake disaster response purposes. 

While the latter purpose is the motivation of this work, we hope that the global building inventory 

database described herein will find widespread use for other mitigation efforts as well. 

For a real-time earthquake impact alert system, such as U.S. Geological Survey’s (USGS) 

Prompt Assessment of Global Earthquakes for Response (PAGER), (Wald, Earle and others, 2006), 

we seek to rapidly evaluate potential casualties associated with earthquake ground shaking for any 

region of the world. The casualty estimation is based primarily on (1) rapid estimation of the 

ground shaking hazard, (2) aggregating the population exposure within different building types, 

and (3) estimating the casualties from the collapse of vulnerable buildings. Thus, the contribution 

of building stock, its relative vulnerability, and distribution are vital components for determining 

the extent of casualties during an earthquake. 

It is evident from large deadly historical earthquakes that the distribution of vulnerable 

structures and their occupancy level during an earthquake control the severity of human losses. For 

example, though the number of strong earthquakes in California is comparable to that of Iran, the 

total earthquake-related casualties in California during the last 100 years are dramatically lower 

than the casualties from several individual Iranian earthquakes. The relatively low casualties count 

in California is attributed mainly to the fact that more than 90 percent of the building stock in 

California is made of wood and is designed to withstand moderate to large earthquakes (Kircher, 

 

1

 U.S. Geological Survey, National Earthquake Information Center, 1711 Illinois St. Golden, CO 80401 (contracted 

through Synergetics Incorporated - http://www.synergetics.com). 2

 U.S. Geological Survey, National Earthquake Information Center, 1711 Illinois St. Golden, CO 80401. 

1
Seligson and others, 2006). In contrast, the 80 percent adobe and or non-engineered masonry 

building stock with poor lateral load resisting systems in Iran succumbs even for moderate levels of 

ground shaking. Consequently, the heavy death toll for the 2003 Bam, Iran earthquake, which 

claimed 31,828 lives (Ghafory-Ashtiany and Mousavi, 2005), is directly attributable to such poorly 

resistant construction, and future events will produce comparable losses unless practices change. 

Similarly, multistory, precast-concrete framed buildings caused heavy casualties in the 1988 

Spitak, Armenia earthquake (Bertero, 1989); weaker masonry and reinforced-concrete framed 

construction designed for gravity loads with soft first stories dominated losses in the Bhuj, India 

earthquake of 2001 (Madabhushi and Haigh, 2005); and adobe and weak masonry dwellings in 

Peru controlled the death toll in the Peru earthquake of 2007 (Taucer, J. and others, 2007). Spence 

(2007) after conducting a brief survey of most lethal earthquakes since 1960 found that building 

collapses remains a major cause of earthquake mortality and unreinforced masonry buildings are 

one of the most vulnerable building stock throughout the world. Hence, it becomes clear that 

mapping out the extreme variations of the vulnerabilities in global building inventories is essential 

for both long-term earthquake loss mitigation and for rapidly identifying disasters. Unfortunately, 

information about the global building stock and its vulnerability is very limited and most often 

nonexistent. Building inventory and vulnerability data are publicly available only for handful of 

countries or regions around the globe. 

This report describes the procedure that has been adopted as a first pass at the development 

of a global building inventory database. The inventory development consists of estimates of the 

fractions of building types observed in each country, their functional use, and average day and 

night occupancy. Various data sources exist that provide building-specific information at a local or 

regional level with varying degrees of confidence; however, few data sources have been found to 

be relevant, consistent, and useful to our needs on a global scale. The inventory development 

methodology presented in this report not only compiles data from various sources but also allows 

us to rate and select the best source based on its vintage and quality. The building-specific 

inventory distribution developed is necessary for the casualty estimation methodology used for the 

U.S. Geological Survey’s Prompt Assessment of Global Earthquakes for Response (PAGER) 

system. 

The database developed during this investigation has been made available as an electronic 

supplement (Appendix VII) of this report. The database contains four tables, each reflecting a 

combination of urban or rural, residential or non-residential category. The fraction of building 

types or dwellings and their occupancy characteristics have been collated for each country to 

represent a country-specific distribution (represented by a row in each of the four table specific to a 

country and is also representative of population exposure) by PAGER structure types. As more data 

become available, we expect that the existing online inventory database will get replaced in parts 

with better quality data. The inventory database developed herein exists in the public domain, 

subject to peer review, scrutiny, and open enhancement. The database is available in most common 

accessible and readable format (Microsoft Excel spreadsheet) for the worldwide user community. 

Having the electronic supplement of this database will not only help users to access it online but 

will also allow the possibility of enhancing the quality of data through an open review and 

development process. 

Introduction 

The U.S. Geological Survey’s (USGS) PAGER system (Wald and others, 2006) produces 

ShakeMaps shortly after all global earthquakes (M > 5.5); the system then produces alerts 

2
indicating the population exposed to each unit intensity level. PAGER is now being enhanced to 

allow rapid loss-estimation capabilities. Standard engineering approaches for loss estimation (with 

explicit modeling of damage to the built environment) require knowledge of affected building 

stock, its spatial, structural, and occupancy characteristics, yet such data are few and far between. 

Some of the existing data sources for inventory database development include the 

EMERCOM database developed by Russian Federation (Shakhramanian, Larionov and others, 

2000), housing data compiled by the statistical agency of United Nations (United Nations, 1993), 

data on durable housing compiled by UN-HABITAT (2007) based on the Demographic and Health 

Survey (DHS), data compiled by Population and Housing Censuses of individual countries (United 

Nations, 2005), and the World Housing Encyclopedia (WHE) database developed by EERI (EERI, 

2007). There also exist country-specific inventory databases compiled by various reinsurance 

agencies such as Munich3 and Swiss Re4, and catastrophe modelers such as RMS5 and EQECAT6. 

In addition to these potential sources, inventory data can also be extracted from individual countryor region-specific research studies. Among these, the research documents that are compiled after 

any large earthquake or developed for regional or national loss estimation studies are often quite 

useful. Alternatively, the inventory databases compiled by insurance and reinsurance industries 

have been developed with varying degrees of relevance (either with limited coverage in terms of 

geographic location or applicable for specific, often limited to insured, facility types) and for 

varying applications (mostly financial impact and loss estimations for insured exposure). In 

addition, most of these developments are not easily accessible or not available for use due to their 

proprietary nature. Similarly, human settlement and inventory databases, such as EMERCOM, are 

not publicly accessible either for verification or for use in other approaches of global earthquake 

risk mitigation. 

Reliable inventory information in the form of building stock, their structural systems, and 

occupancy characteristics forms the requisite input for damage and loss estimation studies. 

Limitations in inventory data generally have significant impact on other aspects of earthquake loss 

and risk estimation studies (ATC., 1985; Coburn and Spence, 1992; Shakhramanian, Larionov and 

others, 2000; Bommer, Spence and others, 2002; FEMA, 2006). The present investigation is 

focused on creating an open, peer-reviewed database of the global building stock distribution that 

contains the critical spatial, structural, and occupancy-related information necessary for casualty 

estimation models for the PAGER project. As the development of this database is based on a 

systematic approach across all regions and countries, it is expected that additional modification and 

improvement of the existing data in this database could be most easily carried out and verified in an 

open, global data development environment. 

Several casualty estimation studies in the past have noted the critical importance of 

knowledge of regional building inventories for rapid earthquake impact assessment (Coburn, 

Pomonis and others, 1989; Shiono, Krimgold and others, 1991a; Stojanovski and Dong, 1994); and 

many have suggested the need of development of either a regional database or a database for 

earthquake-prone areas of the world (Shiono, Krimgold and others, 1991b). The motivation for 

deriving a global residential building inventory database is from a practical need to characterize the 

building stock distribution and estimate the vulnerability of building stock to earthquake shaking. 

This would help in estimation of likely fatalities as part of an effort to rapidly predict ground 

 

3

 Munich Reinsurance Agency (http://www.munichre.com) 

4

 Swiss Reinsurance Agency (http://www.swissre.com) 5

 Risk Management Solutions (http://www.rms.com) 

6

 EQECAT (http://www.eqecat.com) 

3
shaking and earthquake impact globally. This is one key objective for the Prompt Assessment of 

Global Earthquakes for Response (PAGER) program of the USGS’s National Earthquake 

Information Center (Wald, Earle and others, 2006). Among the factors that contribute to heavy 

casualties from significant earthquakes, vulnerable building stocks remain the main cause of 

concern (Coburn and Spence, 2002). Consequently, we require at least a first-order approximation 

of building inventory distribution in all earthquake-affected regions in order to estimate the likely 

number of building collapses that may take place during an earthquake. Beyond this specific 

application, we also expect that such an inventory database may also encourage researchers or 

stakeholders from different countries to develop more refined regionally specific inventory 

databases to augment our efforts. Such developments or verification will potentially help the future 

updates to the PAGER inventory database and improve casualty estimation models in general. 

In our inventory database development, we compile the building inventory information in a 

three-phased approach. In the first phase, we identify various databases that provide mainly 

residential housing building stock distributions for individual countries and then map the raw 

description about construction material or building types in terms of PAGER construction types 

(Appendix I). At this point, we also assess and rate their quality based on the data source and the 

rigor of the source’s original compilation. In the second phase, after preparing the individual 

databases, we then merge them together and pick the highest rated (quality) information to produce 

a single database without mixing or modifying individual input. In the third and last phase, we 

identify and develop housing attributes for missing elements (that is structural inventory 

distribution or occupancy) of countries with no or poor quality data by identifying the most 

appropriate neighboring or analog country for those missing data. This selection is based on rating 

and vintage of all the neighboring countries’ data in conjunction with subjective judgment for 

assignments of missing attributes from selected neighbors. While performing the country-pairing 

assignments, we only use the distribution of housing stock as a possible representation of the 

housing stock distribution for a missing country and not the actual number of housing types or units 

of the neighboring country. Finally, we develop a single inventory matrix for global housing stock 

by country, while allowing for the possibility of easy updates in the future for higher quality data 

for individual countries as well as finer resolution subdivisions within a country (for example, 

inventory by city or administrative units within a country). The global building inventory 

developed in this report has several limitations, and we are currently considering various 

approaches that may improve these deficiencies, including input from the WHE country-specific 

contributions (Porter, Greene and others, 2008). 

Inventory Literature and Data Review 

Various loss estimation methodologies at both the regional and national level developed in 

the past rely on inventory information with a primary focus on accurate assessment of the 

economic impact of an earthquake. The inventory methodology developed by the Applied 

Technology Council’s ATC-13 project, and incorporated in the database development in Federal 

Emergency Management Agency’s (FEMA) loss assessment software HAZUS (FEMA, 2006), 

uses a consensus-based approach. The ATC-13 approach, which mainly focuses on the existing 

facilities in California, classifies the building stock both in terms of structural class (36 types based 

on the structural system) and occupancy class (78 types based on the facility’s use) (ATC., 1985). 

The Level 1 procedure in the ATC-13 approach consists of identifying a unique combination of 

postal ZIP code, Social Function Classification, and Earthquake Engineering Facility Classification 

and then completing all or most of the elements of their inventory matrix. Level 2 and 3 procedures 

consist of completing the missing elements of the inventory matrix for which data are not available 

4
from existing facility databases. The classification scheme proposed in the report has been 

developed in collaboration with engineering professionals from California. Various facilityspecific, pre-compiled databases have been used in the inventory development. These are Census 

of Population and Housing, tax assessors’ records used for development of residential building 

inventory, and the Dun & Bradstreet database at the census block level aggregated by the Standard 

Industrial Classification (SIC) used for nonresidential environment, high-rise building database, as 

well as Government and critical facility databases. The inventory developed for HAZUS software 

is applicable for the U.S. and is based on a similar principle of building classification, except that it 

uses 28 occupancy classes (FEMA, 2006). The HAZUS methodology uses the most recent census 

to estimate square footage for each occupancy class and census tract in the country. It also uses 

characteristics relationships (developed in part via conducting local workshops), between model 

building types and occupancy classes to estimate the fraction of square footage for each of the 

model building types. Thus, both the ATC-13 and the HAZUS building inventories require a 

relationship between model building type and occupancy class on the basis of floor area. 

Development of such a relationship requires a high level of expertise and knowledge about the 

variation of building structure types across a broad range of occupancies throughout the region. 

Estimation of square footage distribution using Occupancy vs. Structure type distribution sought 

from experts does have some serious limitations as shown from a pilot study of inventory 

development carried out for the Manhattan area in New York by Mylonakis and others (2000). The 

inventory methodology used in ATC-13 and HAZUS development was peer reviewed and has been 

designed to take advantage of data available from various sources; it will also accommodate 

various future modifications that may be necessary in augmenting the HAZUS modeling needs. 

However, the development of model building type or occupancy characteristics from square 

footage data in ATC-13 and HAZUS relies on expert opinion and thus requires validation. 

Similarly, it is extremely difficult to gather such expertise at a global scale for each country or 

earthquake prone region. 

The earthquake consequence forecasting methodology developed by EXTREMUM 

developers (Shakhramanian, Larionov and others, 2000) uses inventory data extracted from 

population and building information in 89 regions with 2800 administrative units of the Russian 

Federation. The authors have indicated the distribution of different building types within a town or 

district and the distribution of people in the different building types during the day and night; 

however, the exact procedure of inventory development has not been described in detail. It is also 

unclear as to whether the building type distribution and population exposure data have been field 

validated. A version of EXTREMUM called QUAKELOSS and created by researchers of the 

Extreme Situations Research Center in Moscow is currently being used by World Agency of 

Planetary Monitoring and Earthquake Risk Reduction (WAPMERR) 

(http://www.wapmerr.org/origin.html). 

A questionnaire-based procedure was used by Petrovski (1983) to estimate the fraction of 

building types in several Middle Eastern countries and to understand the associated vulnerability 

characteristics in the region, for the purpose of planning for future needs. The information gathered 

through questionnaires, however, suggested that only 8 out of 10 countries were able to contribute 

to the data development on building distributions, and practically no corresponding data were 

available on the type of engineered structures or other facility types. 

A scenario-based building loss and casualty estimation model developed by Bogazici 

University (Erdik and Aydinoglu, 2002; Erdik, Aydinoglu and others, 2003; Erdik and Fahjan, 

2006) for estimating a worst case scenario of earthquake on main Marmara fault near Istanbul, 

Turkey, utilizes building inventory and population information from the State Statistical Institute. 

5
The system uses building stock data classified primarily based on structural systems and the 

number of stories and age of construction, both determined on a fine spatial grid (approx. of 400 m 

x 600 m) covering 28 districts. The day and time population was estimated using the Istanbul 

Transportation Master Plan of the Istanbul Metropolitan Municipality. 

Other loss estimation models such as a GIS-based computer program, “Early PostEarthquake Damage Assessment Tool” (EPEDAT), designed to produce regional damage and 

casualty estimates for southern California, use inventory data from county assessors’ records for 

buildings, and housing census and employment data for exposed populations (Eguchi, Goltz and 

others, 1997). EPEDAT inventory data include building location, age, use, height, and structural 

type of buildings developed from data provided by county assessors for five counties of southern 

California. However, the exact procedure used to determine day and night time population 

distribution, building type classification, and their occupancy characteristics for six counties has 

not been discussed in detail by the authors (and remains proprietary in nature). Recently, 

Geoscience Australia has launched a two-phase project for development of national building 

exposure database (Nadimpalli, Cornish and others, 2002). The first phase consists of residential 

building inventory data development for Australia using data sources including a nationally geocoded address file developed by the Public Sector Mapping Agency (PSMA) along with buildingspecific data (for example, address, number of occupants, type of dwelling and its valuation) from 

the Australian Bureau of Statistics (ABS) and the Australian Housing Survey (AHS). The second 

phase consists of development of a building inventory database for commercial and industrial 

buildings which is still in the design phase. It is worth noting that inventory development at a 

country or even regional level requires substantial efforts both in terms of identification and data 

compilation. In many countries, the base inventory data necessary for completion of a loss-database 

do not exist, are not available, or are in inaccessible forms. 

Recent developments in inventory development at regional levels for earthquake loss 

estimation include the ongoing LESSLOSS project, which was launched by European Centers of 

Excellence in earthquake and geotechnical engineering, focusing on risk mitigation for earthquakes 

and landslides in European countries. The LESSLOSS project addresses natural disasters, risk and 

impact assessment, natural hazard monitoring, mapping and management strategies, improved 

disaster preparedness and mitigation, development of advanced methods for risk assessment, 

methods of appraising environmental quality and relevant pre-normative research 

(www.lessloss.org). 

Another ongoing European project, Network of Research Infrastructures for European 

Seismology (NERIES), also focuses on development of a new generation of hazard and risk 

assessment tools designed to improve monitoring and understanding of the earthquake process. 

NERIES will combine networking, transnational access and joint research activities to promote 

improved access to distributed databases, common protocols, standardized procedures and 

strategies for long-term archiving and distribution of seismological data. The project will mainly 

strengthen the role of European seismology in global seismic monitoring and hazard mitigation 

(http://neries.knmi.nl). One of the products by NERIES which is under development is a loss 

estimation tool similar to HAZUS. 

In order to develop an inventory of building types, several key data sources have been 

identified that provide information in various forms, as shown in table 1. Each of these sources has 

been studied in detail for the information content and its usability in the PAGER framework. The 

World Housing Encyclopedia (WHE) database covers 110 housing types (as of Jan. 10, 2007) 

contributed by 180 volunteer engineers and architects from various countries and regions 

6
(http://www.world-housing.net/). Each of the forms covers information pertaining to structural, 

occupancy, and socio-economic characteristics of a particular building type reported by WHE 

volunteers. The information available in WHE forms is peer reviewed by international experts of 

the respective field and region. 

The United Nations and its various agencies (such as UN-HABITAT7

, UN Dept of 

Statistics8

, Urban-Info database9) have been compiling demographic or housing data such as 

population, number of households and housing types, and its construction material for decades. 

Housing data compiled by UN agencies not only assist local governments in reviewing housing 

conditions in their own countries but also offer users a comprehensive overview of housing and its 

statistics worldwide. The main sources of UN housing data are official census publications of 

individual countries dealing with housing characteristics of the population. According to UN 

Statistics Department, during the 1990 round of census, about 197 countries have collected 

statistical data on housing through a census or through a survey as a part of the census. However, 

many of these countries did not compile data related to distribution of housing types by 

construction material (the materials used for construction of roof, wall, or floors of a dwelling). The 

UN (1993) housing data publication provides data on construction material used for wall 

construction for 40 countries (most of the countries in the African continent). UN-HABITAT 

(2007) database on durable housing provides information on fraction of housing types by material 

used for construction of wall/roof/floor based on Demographic and Housing Survey (DHS). 

Despite the availability of data on the fraction of dwellings with floor or roof construction material 

for many countries, the data could not be used directly to deduce the construction types. In general, 

the construction material used for building a structural system (mainly walls in case of a gravityload-bearing system) that supports roof and floor elements of a dwelling is useful for mapping the 

construction types. Additional information is necessary in order to estimate a structural system 

from roof or floor material dataset which varies from country to country and is not easily available. 

Similarly, most of the census surveys or UN sponsored surveys, as just discussed, also provide 

information in terms of housing types (conventional, temporary or marginal dwellings) as 

mentioned in subsequent sections. However, additional knowledge about distribution of each of 

housing types into various construction types is generally not provided and thus was not available 

for PAGER database development. 

Inventory Data Sources 

We have performed a short but intensive search to understand what data exist 

internationally about the nature of building stock and the way it has been developed and also 

sources of information from which the data may be gathered. This section presents a short 

summary of various databases that have been identified to be potentially used for PAGER 

inventory development. 

UN Statistical Database on Global Housing (1993) 

 One of the most important sources of data on global residential housing stock has been 

prepared by the United Nations Statistical Division (UNSD), Department of Economic and Social 

Information and Policy analysis of the United Nations Secretariat (United Nations, 1993). The 

purpose of the database is mainly to assist the governments of respective countries in reviewing 

 

7

 United Nations HABITAT project (http://www.unchs.org ) 

8

 United Nations Department of Statistics (http://unstats.un.org/unsd/demographic/sources/census/default.aspx) 

9

 United Nations Urban Info Data Center (http://www.devinfo.info/urbaninfo) 

7
housing conditions. This database also provides a comprehensive and graphical overview of 

housing in the world and guides users to sources of housing statistics worldwide. According to UN 

(1993), the main source used for compilation of the housing data was the official census 

publications dealing with housing characteristics of the population. However, when data were not 

published in the census series, UN compilations were based on other sources such as statistical 

yearbooks of the country, which also provide housing information. 

The “Compendium of Human Settlements Statistics 2001” covers 243 countries and 

includes “housing” as one of its five main sections (United Nations, 2001). The UNSD also lists the 

dates for recent country-level housing and population census and gives Web links to some of these 

data. Some countries have carried out population surveys but have not included housing surveys in 

their census. The UN (1993) publication on housing-type data covers 136 countries; however, 

PAGER’s structure type distribution database compilation was not based on housing type. This is 

mainly because housing type description does not represent the structural system; rather, it only 

provides information about dwelling type, for example, temporary, conventional, marginal or basic 

dwellings. Such description could not be utilized immediately into PAGER inventory development 

unless further classification of each type of dwelling by its construction material or structural 

system becomes available. Figure 1 illustrates a broad description of inventory by external wall 

material of occupied dwellings in Hungary. It is obtained from 2 percent of the representative 

sample study conducted by Hungarian Central Statistical Office and later compiled by United 

Nations publication (United Nations, 1993). The description of construction material such as 

adobe, wood, or concrete, provides useful information about likely construction type. 

A housing unit is a separate and independent place of abode intended for habitation by one 

household, or one not intended for habitation but occupied as living quarters by a household at the 

time of census or other inquiry. Housing units consist of both conventional and unconventional 

dwellings. About 44 countries had data for construction material of external walls in the UN (1993) 

publication, which refers to the construction material of the outer walls of the building in which the 

living quarters are located. If the outer walls are constructed of one or more materials, the 

predominant material is usually indicated. This database has several limitations, as follows: 

• It does not represent the complete structure type distribution (residential versus 

nonresidential building stock). 

• It represents housing units (in which several housing units could be part of a single 

building) by construction material rather than by the fraction of buildings by construction 

material. 

• For some countries, it utilizes colloquial names for the construction material instead of 

uniform nomenclature across the countries (for example, “quirintin” in some African 

countries which refers to woven branches and straw used as construction material). This 

makes the mapping exercise laborious in terms of searching and mapping each and every 

individual terminology of the local construction material with standard or more common 

nomenclature of construction material. 

• It does not provide information about structural system required to assess the performance 

of building during earthquake ground shaking. Hence it was necessary to make certain 

assumptions while mapping the construction material types with common structure types. 

This was very time consuming and often required higher order assumptions to convert the 

raw database into more uniform PAGER structure type distributions using a predefined 

mapping scheme. 

8
• The development of the database is not based on uniform procedures, and no engineering 

expertise was sought to demarcate common construction. Similarly, no engineering surveys 

were carried out either to develop the fraction of housing type data or to verify existing 

inputs available from individual countries. 

• The database development is not intended to be used for carrying out engineering loss 

estimation studies. 

 Despite all the limitations, this database provided the most basic input about construction 

types for almost 40 countries and hence has been utilized in the PAGER inventory development 

even though we assigned them a “Low” rating level. The need behind using UN (1993) publication 

was to consider all available databases of variable quality in the absence of any particular reliable 

inventory data source at global coverage. Also, the engineering survey-based inventory information 

was available for only handful of regions or countries in the world. We seek to utilize all available 

data sources including the UN (1993) publication and to develop a single database with a uniform 

mapping scheme which also could potentially be useful for future updation. 

UN-HABITAT Database (2007) 

The United Nations Human Settlements Program, UN-HABITAT, is the United Nations 

agency for human settlements. It is mandated by the UN General Assembly to promote socially and 

environmentally sustainable towns and cities with the goal of providing adequate shelter for all 

(http://www.unhabitat.org). We obtained data on durable housing (mainly giving distribution of 

housing types in terms of construction material used for wall, roof or floors) directly from the 

office of Human settlement program of United Nations through written communication. Data are 

available for more than 60 countries and procured through the Demographic and Health Survey 

(DHS) conducted in each country (UN-HABITAT, 2007). Only 22 countries have information on 

the distribution of housing units. Table 2 shows an example dataset for Tanzania compiled by UNHABITAT. The dataset is based on descriptions of the predominant material that has been used for 

the construction of external walls in Tanzania. Data on floor and roof construction materials could 

not be utilized for defining the construction types because the basic structural system is generally 

characterized by the building elements (such as load-bearing walls, beam-columns, moment 

frames, or shear walls) that resist the lateral loading during earthquakes. 

As the database compilation for durable housing in the (UN-HABITAT, 2007) database 

was similar to the procedure adopted for (United Nations, 1993) database compilation, the inherent 

issues related to housing units versus building data; lack of uniformity in nomenclature of 

construction material and lack of engineering expertise in carrying out surveys limit the 

applicability of this database. We assigned “Low” quality to this database and thus it forms the 

basic layer of information about housing in a particular country where no other source of 

information is available. Though both the (United Nations, 1993) and (UN-HABITAT, 2007) 

database have the same quality ratings, most of the data in (UN-HABITAT, 2007) database was 

obtained more recently and hence is considered preferable to the (United Nations, 1993) data for 

any country where both databases are available. 

Housing Census Database (Country-Specific) 

In the 2000 round (the 1995-2004 census decade), about 173 countries conducted housing 

censuses. Still, many countries in the world either have not planned for conducting housing census 

or have not shared the information with United Nations Statistics Division. Among the 131 

9
countries for which the housing census data were available to the United Nations office, only 73 

countries have data related to the construction material of the outer walls of buildings. For many 

countries, the data seem to be available on the Web site of the respective statistical office. In the 

present exercise, we compiled housing census data for eight countries that have made their data 

available online. Table 3 provides a list of countries and the associated Web addresses from which 

the data have been downloaded and compiled for PAGER inventory development. 

The housing census compilations were not carried out by engineering professionals and 

hence the data provide only a limited contribution for engineering characterization of construction 

types. Following common practice, we deduced the information about the housing types from the 

use of the material for construction of the roof, floors, and external walls. Almost all housing 

census uses this indicator to compile information about state and quality of housing in respective 

countries. If the walls were constructed of more than one type of material, the predominant type of 

material was reported. Due to colloquial terminology for defining construction materials, it was 

necessary to perform general Web research to understand the description of each of such variants 

and map them with standard nomenclature. Often we referred to pictures of general housing types 

available online to understand more about these descriptions and then used engineering judgment 

to map them to appropriate PAGER structure type categories. For some terms, there was some 

ambiguity in interpretation due to the lack of adequate information about typical construction; 

however, this uncertainty could be reduced by involving local engineering professionals. Future 

versions of this open database could incorporate such critical inputs to modify and reestimate the 

fraction of those construction types in respective countries. 

Though the housing census surveys are not intended for database development for 

earthquake loss estimation studies, housing census data do represent the characteristics of the 

housing of the entire population and entire geographic extent of the country. The level of details 

and procedures involved in compiling such information is quite exhaustive, and hence we have 

assigned “Medium” quality to data derived from housing census. Table 4a shows typical housing 

characteristics data obtained by the National Statistical office of the Philippines. In the present 

approach, we are interested in estimating the distribution of housing units by construction material 

of the external wall, and data available for Philippines provide direct estimation of these fractions. 

We mapped the description of wall materials to the standard PAGER structure types using 

Appendix IV. For example wood as well as bamboo / sawali / cogon / nipa descriptions in 

Philippines have been mapped into the general wood construction (W) category which forms 45 

percent of total housing stock in Philippines. Another example includes housing census data 

available for Pakistan as shown in table 4b. A similar procedure was used to map and estimate the 

fraction of each mapped PAGER structure types in Pakistan. 

World Housing Encyclopedia Database (Country-Specific) 

The World Housing Encyclopedia (WHE) is a joint initiative of Earthquake Engineering 

Research Institute (EERI) and International Association for Earthquake Engineering (IAEE) to 

develop an interactive Web-based encyclopedia of housing construction types in seismically active 

areas of the world (EERI, 2007). The database contains a comprehensive global categorization of 

characteristic housing construction types across the world. The housing type report includes all 

relevant aspects of housing construction, such as socio-economic issues, architectural features, 

structural system, seismic deficiencies and earthquake-resistant features, performance in past 

earthquakes, available strengthening technologies, building materials used, construction process 

employed, and insurance conditions. In addition to the structure-specific information, several 

illustrative photos, drawings, sketches are also included in the report. However, despite such 

10
elaborate information, the WHE database does not contain inventory and vulnerability-specific 

information for all the structure types, which are of importance to PAGER development. 

Subsequent sections will describe the details of housing type database compilation along with 

occupancy and inventory information extraction from each WHE housing type form available in 

the WHE database. 

Data Compiled from Published Literature 

Published literature about earthquake vulnerability or loss estimation methodologies 

provides important input for assessing the inventory characteristics of specific countries. Several 

research studies in the past have dealt with challenges of regional inventory compilations or 

country-specific building vulnerability characterization (for example, ATC-13, HAZUS, 

KOERIloss). In addition to these sources, other region-specific studies such as Petrovski (1983) 

and EQRM development (Sinadinovski, Edwards and others, 2003; Robinson, Dhu and others, 

2006) provides country-specific building characteristics and distributions. Although, some of the 

loss modeling applications is available online, the inventory specific information is generally not 

available for public use. In the present investigation, we utilized publicly available information for 

building inventory as an additional data source to PAGER inventory development. As most of the 

data in published literature on inventory have been peer reviewed by engineering professionals, we 

assigned a “High” quality to them. Table 5 provides a list of countries and the associated data 

source (published article or loss assessment tool) and it’s vintage. At present, the list covers only 

limited countries; however, we expect that it will grow as we collect more data from published 

literature for individual countries. 

As the data from most of the loss estimation studies have already been mapped using 

standard model building types classification schemes (for example, HAZUS model building types 

for the U.S. and California), it was relatively easy to map them into equivalent PAGER structure 

types using engineering judgment. Table 6 provides a standing mapping scheme that has been 

adopted for structure type mapping for Middle Eastern countries based on the Petrovski (1983) 

inventory distribution for urban areas. The construction type mapping from HAZUS model 

building types to PAGER structure types is discussed in a subsequent section. 

Inventory Development 

Overview 

The inventory data needs of PAGER have a much wider geographic scope than required in 

previous studies. Hence, a number of techniques and processes need to be considered before a 

consensus is made to satisfy regional needs and data constraints. For example, in order to estimate 

the fraction of building types, it was necessary to classify the worldwide building types broadly 

based on predominant construction material used for the construction of the walls and their 

structural systems. The choice of limited categories for building types was mainly due to lack of 

sufficient information about structural systems, and the limited scientific information about 

performance of such building types during strong shaking. Owing to paucity of data, buildings have 

been broadly classified into residential and nonresidential types based on functional use mainly due 

to wide variation of occupancy characteristics between these two broad occupancy categories 

during day and night time. 

11
PAGER Specific Inventory Needs 

In order to estimate building damage and related casualties due to an earthquake anywhere 

in the world, it is essential that the building types, their distribution within a country or region, and 

their average occupancy during day and night time be determined. PAGER utilizes the population 

exposure as derived from the Oak Ridge National Laboratory’s LandScan 2005 database (e.g., 

Dobson, Bright and others, 2000; Bhaduri, Bright and others, 2002) as the basic input for 

estimating the likely number of people exposed to variable ground shaking in terms of different 

ground motion parameters (e.g., peak acceleration, velocity and spectral response parameters). The 

derivation of ground motion shaking distribution is computed using ShakeMap system (Wald, 

Worden and others, 2005). Within the framework of the PAGER semi-empirical (Jaiswal and 

Wald, work in progress) and analytical loss models (Porter, 2008), the following are the broad 

requirements related to development of global building inventory: 

1. The fraction of each dwelling type by occupancy (based on functional use), 

2. Occupancy (number of people per facility class during the day, night and transit time) 

characteristics, and 

3. The fraction of PAGER model building types (construction types based on structural 

systems) for each occupancy class. 

Building inventory classification allows us not only to differentiate buildings with 

substantially different structural characteristics but also to group them based on availability of raw 

data. The available data could be in the form of predominant construction material used or model 

building types distribution compiled through engineering surveys or expert opinion. Ideally, each 

model building type and its vulnerability characteristics would be ascertained, giving the fraction of 

each model building type in a region. HAZUS classifies the entire building stock within the U.S. 

into 36 model building types, yet estimation of the inventory for each of the model building type is 

not based on actual data but has been derived based on very general tax assessment and housing 

census information. The model building types used within HAZUS are conditioned to only U.S. 

building designs and construction practices and therefore cannot be used for classifying inventory 

in developing countries of the world, where construction practices are significantly different. For 

the PAGER loss methodology applications, building inventory distribution needs to be mapped into 

PAGER model building type taxonomy as shown in Appendix I. 

Methodology 

The PAGER inventory development was divided into three phases: 

1. Database identification, preparation and confidence rating, 

2. Data aggregation and quality ranking, and 

3. Data assignment for missing entries. 

Figure 2 provides an overview of the proposed inventory development methodology for 

PAGER development. The inventory development procedure described in following subsections is 

based on housing unit fractions rather than building fraction of particular structure type. Some of 

the data sources such as, published literature or WHE data provide building inventory distribution 

by building rather than dwellings. In such cases, we have identified them separately and converted 

into fraction of dwelling stock by equivalent number of units or by volume, rather than its number 

for the PAGER inventory distribution. 

12
Phase I: Database Identification, Preparation, and Confidence Rating 

Phase I constitutes a search of all the country-specific available information from various 

identified inventory data sources and processing of the raw data through attribute mapping and 

assigning qualitative ratings to each data source to be used for later phase of inventory 

development. 

Data Identification 

We first identified all available data sources that could provide useful information related to 

the PAGER building inventory development (the WHE database, UN Housing database, published 

literature or reports, individual country housing information) as shown in figure 3. The selection of 

the data source is based primarily on whether or not the data provide either occupancy 

characteristics or construction type information, at least at the country level. 

Geographic Location and Resolution 

After selecting a database, we confirmed that the database provided appropriate geographic 

coverage. If data were only available for sub-country regions, in some cases they were used as a 

proxy to represent a particular country, but that assumption was reflected by a lower confidence 

level assignment used in the inventory model. For example, post-earthquake or scenario earthquake 

studies, for which the distribution of construction types or occupancy characteristics were obtained 

via field surveys (Faccioli, Pessina and others, 1999; Ozmen, 2000; Tien, Juang and others, 2002; 

Tobita, Miyajima and others, 2007), were sometimes considered to be reliable and generally 

applicable to the country as a whole, particularly in the absence of country-level data. Data that did 

not contain country-specific information or that do not help in making country specific expert 

judgment were discarded at this stage. 

Occupancy Classification 

The occupancy-specific information needed for the PAGER inventory model is generally 

categorized in terms of residential, nonresidential or outside (for example, people in transit and not 

subject to casualties due to potential collapse of buildings) as a function of the time of day. If the 

database provides information about the fraction of people in each of these three occupancy 

categories, then the data are used directly. In the absence of such specific categorization, certain 

predefined classification schemes based on engineering judgment are used to convert the raw data 

into the equivalent three occupancy categories. For example, the fraction of people working in 

various types of facilities, such as administrative, commercial, banks, academia, and other facilities 

with similar usage, given in the database were classified into the nonresidential category, whereas 

people residing in single or multi-family dwellings could be grouped into the residential category. 

The fraction of the population that does not belong to either type is broadly termed as outside. 

Since most of the databases do not have occupancy defined as a function of the time of day, it is 

necessary to apply this correction separately to each occupancy category during the inventory 

development. Note that most of the databases that have been identified for PAGER inventory 

development provide only residential building stock data. However, the inventory methodology 

discussed in the present investigation is not limited to compilation of only residential inventory. It 

is planned to develop nonresidential building stock distribution by construction type in a 

subsequent stage through WHE-PAGER project for inventory development. 

13
Data on day-time population of commercial or industrial buildings, or any other 

nonresidential buildings, are not available for any of the countries. The residential building 

inventory database to be developed here is targeted towards estimating fraction of occupants living 

in the residential dwellings. However, the exact fraction of the population which is not part of 

residential buildings especially during the day is not available. In order to estimate fraction of 

people living in any other unit or outside other than residential (which can be commercial, 

industrial or in transit) housing units during the day, we may need to estimate the total work force 

(labor force) in each country and then assign the fraction of the work-force population into 

nonresidential and outside. 

We first compiled the data on average occupancy (number of persons) per dwelling 

irrespective of the construction type in different countries from several databases such as UN 

(2001) and UNECE (2006) as shown in Appendix II. In order to estimate the fraction of people not 

residing in residential dwelling during work hours, we calculated the work force population from 

EUROSTAT (2006), the CIA-FACTBOOK(2007), and developed an estimate of fraction of the 

population in the workforce as shown in Appendix III. Details of estimating fraction of population 

by time of day in three different occupancy categories (residential, nonresidential and population at 

transit) are described in (Jaiswal and Wald, work in progress). 

This database could be used to estimate the likely average number of dwellings in each 

geographic region based on population data from the Oak Ridge National Laboratory’s LandScan 

2005 database (e.g., Dobson, Bright and others, 2000; Bhaduri, Bright and others, 2002). The 

average number of people per construction type is derived from the WHE housing type database 

and the ATC average occupancy estimation procedure as shown in Appendix VI. 

Construction Type Classification 

An important and challenging effort required for PAGER inventory development was the 

identification of data sources that could provide information about various construction types and 

the fraction of people residing in each type for a given country. Ideally, construction types could be 

classified according to their structural system, load transfer mechanism, predominant construction 

material used, and performance during past earthquakes. However, most of the inventory data that 

have been identified during the inventory development efforts did not provide building-specific 

information; hence it was necessary to adopt broad construction type classification based on 

material used for the construction of walls and roofs. This is similar to the classification used by 

most of the housing census and surveys carried out for a large number of countries in the past. 

More details about construction type classification are given in Appendix IV. 

Average Occupancy by Construction Type 

Earthquake casualty estimates are dependent on the fraction of people residing within each 

building type at the time of the earthquake. A typical wooden single family dwelling has a much 

lower occupancy than a large multistory, reinforced-concrete apartment building. Hence it is 

necessary to estimate the average number of occupants in a particular building type to estimate the 

likely casualties after an earthquake. 

Typical occupancy information by construction type could be estimated by utilizing the data 

developed from Appendix I along with inputs from WHE reports (EERI, 2007) and region-specific 

published literature wherever available. However in the present report, only the WHE forms have 

been used to deduce the average occupancy of a construction type by time of day (day or night). 

14
Because data are so limited, it is necessary to deduce such information by utilizing the average 

person per housing unit available from the UN database along with engineering and demographic 

judgments to estimate occupancy information. We first mapped the WHE housing types to PAGER 

structure type category as shown in Appendix V. We then assigned an estimate of occupancy 

pattern for PAGER structure type using the most commonly described occupancy pattern given in 

WHE database for a particular structure type. For the PAGER structure type where WHE database 

does not provide occupancy-related information, we used expert judgment based on height, as well 

as structural system information from similar construction type in the WHE database, to assign 

likely occupancy pattern. The mapped occupancy pattern for PAGER structure type is shown in 

Appendix VI, and it can be used for countries where occupancy-specific information is missing. 

Attribute Rating 

Once the selected database is processed for these three specific attributes, we assigned 

ratings for each based on predefined PAGER attribute rating criteria (High, Medium, or Low) and 

rated all the entries in a given database. “High” quality refers to data compiled after engineering or 

telephonic surveys; field visits for ground-truth or data compilations from local engineering 

experts; “Medium” quality refers to data compiled by general field survey and assignments 

generally not based on engineering standards, and “Low” quality refers to data compiled by nonengineering agencies and is not specifically meant for engineering loss analysis. 

Any missing information is assigned with “Null” whereas all useful information is entered 

using the PAGER attribute mapping scheme. After rating, we verified each data entry for possible 

errors or duplication and repeated this procedure separately for all possible PAGER inventory 

databases that contribute useful information. At the end of this phase, each raw data source, 

alongwith the relevant information contained therein, was identified, rated, and compiled for 

further processing in Phase II. 

Phase II: Data Prioritization, Merging, and Country Assignments 

As shown in figure 4, Phase II consists of the development of single inventory matrix that 

covers the structure and occupancy related information at the country level by selecting the best 

quality attributes from the various individual databases processed during Phase I of the PAGER 

inventory development. 

Selection of Attribute Assignments Using Ratings and Vintage of Data 

Source 

For each country, if only a single database provides information for a certain attribute, we 

assign it directly in the PAGER inventory matrix along with its rating. If none of the databases 

provide information for certain attribute of a country, we assign the missing information based on 

an a priori country pairing described in the next section. If multiple databases are available for a 

particular country and attribute, then selection of appropriate attribute assignment is carried out in a 

two-step process according to both its rating and vintage. We sort the attribute assignments 

according to their rating and select attributes that have higher rating. In the case of a particular 

attribute for a chosen country that has multiple databases assigned the same rating, we sort the 

assignments according to database vintage, retaining the most recent data. If attributes with similar 

ratings and vintage exist, we chose the attribute based on heuristics i.e., published literature data 

supersedes World Housing Encyclopedia reports or expert judgment surveys and UN-HABITAT 

15
(2007) data supersedes UN (1993). The outcome of this exercise is selection of a single attribute 

assignment for a chosen attribute type and country. 

Attribute data with “Null” or missing entries are carried forward for further processing in 

the Phase III procedure. The output of Phase II consists of a single inventory database matrix 

compiled from several individual data sources based on PAGER prioritization and classification 

criteria as shown in figure 5. 

Phase III: Data Development for Missing Entries and Synthesis 

The main objective of this phase is to fill out all the non-exhausted attributes (“Null” 

entries). This is done either by assigning all the PAGER attributes for a particular country based on 

predefined criteria (deduced normally from attributes of a neighboring country) or by assigning a 

regionally consistent attribute-specific judgment due to lack of specific information in the available 

databases. The following are the steps involved in filling up “Null” entries. 

Missing Entries and Country-Pairing 

For each missing entry of the inventory matrix, we first verify whether any regional 

estimates are available for the missing attribute. Regional estimates may be in the form of data 

published in research articles or reports applicable for a group of neighboring countries where the 

available estimates could be used as a proxy. Earthquake source-specific or regional scenario-based 

loss estimation studies are generally performed by researchers to understand the potential of future 

large earthquakes and its impact on human built environment. Such studies make use of localized 

survey or expert judgment to develop broad understanding of building inventory distribution of a 

region under consideration. Although these data may or may not necessarily reflect country-level 

loss estimates, in absence of any additional information, such peer-reviewed inventory estimates 

can be used for replacing missing data for those countries. 

Despite the use of several data sources during PAGER inventory development, more than 

50 percent of countries across the globe have no direct information about building inventory 

distribution (fig. 4). This step is aimed to develop first-order estimation of distribution of housing 

types in countries that have no information available. In the absence of any regional data or 

inventory of other loss estimation studies for the region/country, we needed an approach that could 

assign the missing data for countries through similarities of demographic and economic 

characteristics, construction practice in the neighboring countries, and also performance of 

facilities during past earthquakes to base our engineering judgments for country-pairing. Porter and 

Jaiswal (2006 unpublished report) compiled a first order vulnerability ranking scheme by which to 

group countries or a geographic regions of the world were classified into 5 vulnerability regions. 

Each of the vulnerability regions was assumed to have relatively uniform vulnerabilities 

independent. The building stock and infrastructure belonging to region 1 was assumed to be the 

least vulnerable (e.g., California, New Zealand) region 5 countries were highly vulnerable (e.g., 

Afghanistan, Iran). The basis for the regionalization was based on building codes and code 

enforcement. Countries with few inherently lethal pre-code buildings are assigned region 1. 

Locations with poor-engineered construction are assigned region 3. Adobe and rubble masonry 

being among the most lethal types, locations where these types predominate are assigned 5. Levels 

2 and 4 are intermediate. Although the scheme is broad, non-quantitative and based on subjective 

judgment, it is being used only to select the best neighboring country for assignment of building 

inventory. It is quite common to have similar construction practice and building characteristics 

among neighboring countries that have similar population growth pattern, lifestyle, and social and 

16
economic characteristics. We compiled a list of countries that have no data on building inventory 

and developed country-pairing criteria to replace missing elements with known information from 

neighboring countries. 

PAGER Inventory Matrix Compilation 

Among all the neighboring countries, we selected the country that has same PAGER 

vulnerability region code (Porter and Jaiswal, 2006 unpublished report) as the vulnerability of 

missing country. If there are no countries with the same vulnerability code and or more than one 

country with the same PAGER vulnerability code, we use the data from the country that has 

highest rating (fig. 6). In the case of countries that have the same ratings, we selected the neighbor 

that has most recent data (fig. 7 & 8) and replace the data of the missing country element with the 

information from the chosen country. After completing the first-level exercise of identifying the 

neighbors that have the highest quality data and which share a common boundary with the missing 

country, several countries remained with no immediate neighbor. In such cases, we used expert 

judgment to identify the country from the region (without common boundary) that had the highest 

quality and most recent data. Finally, we used the procedure described in the previous section to 

replace all the missing attributes of inventory matrix of a particular country and then repeated the 

procedure for all the countries to replace all “Null” entries for the final PAGER inventory matrix as 

shown in figure 9. The outcome of Phase III procedure is a single PAGER inventory matrix with 

the three PAGER attributes, namely occupancy distribution, construction type distribution, and 

occupancy by construction type for all the countries of the world. Appendix VII (also available as 

an electronic supplement) shows a country-level PAGER inventory database compiled in terms of 

fraction of PAGER structure types for each country along with the source, rating, and vintage of 

the database. 

Limitations of the Methodology 

The methodology discussed in the previous section for our global inventory development 

for the PAGER project is similar to the ATC-13 and HAZUS inventory development apart from its 

global coverage and aggregation for the types of attributes involved in the inventory development. 

The three-phase methodology described in this investigation is relatively new and largely untested. 

Hand checks were done for certain attributes at the country level and QC/QA procedure adopted 

during data development. However, some modifications in terms of the choice of attributes, low 

geographic resolution for certain countries, and attribute mapping are likely to be required. Many 

assumptions such as the choice of attributes and their mapping were based on engineering 

judgment followed by limited Internet research (in terms of photos or specific reports of housing 

types for a particular country). For example, figure 10 shows a typical wood construction in 

Bangladesh. These assumptions could be improved upon through sampling of actual facilities or by 

conducting engineering surveys. Although the methodology is designed to work for a global 

inventory, users should be aware of the limitations of the methodology and be on the alert for 

obvious discrepancies in the raw or processed inventory data. 

The inventory developed using this methodology is generally dependent upon the raw data 

sources. The quality and accuracy of individual raw data sources and its interpretation during 

inventory development will become increasingly unreliable for resolutions finer than the country 

level unless more refined geographic regions are identified. Rapid growth of urban centers, 

particularly in developing countries, could result in significant variations of construction practice, 

choice of construction type, urban planning, and unbalanced demographic and social characteristics 

17
(fig. 11). As the inventory developed in the present investigation is open for review and 

modification, these limitations are bound to diminish as more data become available at higher 

resolution. 

Illustration of Inventory Data Compilation for Peru 

This section illustrates the housing stock attribute grouping assignments and compilation of 

the inventory data for a chosen country, Peru. The following data sources were consulted: 

1. United Nations housing database 

2. UN-HABITAT (2007) DHS database 

3. Population and Housing Census 

4. World Housing Encyclopedia (WHE) housing type reports 

5. Published Literature about housing/building stock 

United Nations Housing Database 

As shown in figure 12, the housing related information is limited to the knowledge of 

material of construction of external walls of a housing unit for Peru. The nomenclature used herein 

(such as, waste, other) is not explicit and consistent, which makes it difficult to collate directly to 

particular structure types. We mapped this distribution to PAGER structure type using assumptions 

as discussed in Appendix IV. About 31.1 percent housing units in Peru have been constructed using 

bricks for wall construction. However, brick could also be used as a potential building material for 

construction of infill masonry walls in reinforced concrete or steel-framed constructions. Similarly, 

timber-framed structures also commonly utilize brick-veneer as a cladding material. Since the 

original data do not clearly indicate the fraction of each of these categories, we conducted a 

literature search for housing construction in Peru and found that most of the constructions in Peru 

were constructed using non-engineered weak masonry structures with brick as the construction 

material (fig. 13). We avoided any expert judgment in evaluation of the fraction of units of 

different categories in this phase of inventory compilation and tried to directly use original input 

from the source for mapping. Hence this fraction of housing units have been classified as 

unreinforced fire-brick masonry (UFB) category and similarly, the 7 percent fraction of housing 

units which are made of wood have been mapped to general timber construction (W) category. 

Since no specific details are available about “Waste” or ”Other” type material, we have classified 

this housing stock to be of informal constructions (INF) category. 

UN-HABITAT (2007) DHS Database 

The demographic and health survey dataset compiled by UN-HABITAT provides data for 

Peru, collected in 2004, and shown in table 7. The raw descriptions of the wall materials are quite 

difficult to map with the conventional terms used for describing the material of construction. 

Again, some background literature search and Internet research were beneficial in understanding 

each description, allowing us to map them to PAGER construction types, as shown in Appendix 

IV. 

18
Population and Housing Census 

The National Institute of Statistics of Peru conducted a 2005 round of population and 

housing census surveys, covering around 27 million persons and approximately 7 million homes, 

respectively (http://unstats.un.org/unsd/demographic/sources/census/perupdf.pdf). However, the 

official data about census of housing by construction material were not available online to access 

and utilize for inventory development. 

World Housing Encyclopedia (WHE) Reports 

For Peru, the World Housing Encyclopedia (WHE) Web site (http://www.worldhousing.net/) provides three housing type reports, namely, confined masonry buildings; confined 

masonry houses, and adobe houses (EERI, 2007). Each of the reports provides detailed information 

about housing characteristics, construction practice, occupancy (average number of people per 

housing unit/building) and other social and economic aspects. Unfortunately, none of the reports 

for Peru cover the inventory information for the specific housing types as required for PAGER 

inventory database. Because of the lack of specific information, the WHE database could not be 

utilized as possible input for inventory compilation for Peru. 

Published Literature of Housing/Building Stock 

The June 23, 2001, a magnitude 7.9 earthquake near the southern coast of Peru caused 

serious damage and casualties. There was widespread damage and 102 people were killed, 1,368 

were injured, and 11,043 houses were destroyed, primarily in the vicinity of the provinces of 

Arequipa, Moquegua, and Tacna. The survey conducted by the Building Research Institute of 

Japan found three main housing types in rural areas: adobe, sillar blocks, and brick-masonry 

construction (Kusunoki, 2002). According to the report, nearly 80 percent of housing in the San 

Francisco district was made of adobe and most of these dwellings suffered damage during the 

earthquake. The EERI reconnaissance study, conducted after 2001 earthquake (EERI Special 

Earthquake Report, 2001), indicated that the housing in the urban areas were mid-rise reinforced 

concrete buildings with infill-masonry walls or shear-wall construction. The 2007 Peru earthquake 

caused widespread damage to non-engineered structures commonly found in urban and rural areas 

of Peru;this includes traditional earthquake structures (adobe), masonry structures and RC frames 

with infill masonry but not designed for earthquake loading (Taucer, J. and others, 2007). 

However, none of the published literature provides specific information regarding either structure 

type’s distribution or the general building stock distribution for Peru. 

A Review of the above data sources for inventory information for Peru clearly shows that 

although Peru is an earthquake prone country, the knowledge of building inventory is severely 

limited. The most recent information available concerning building stock for Peru is that of UNHABITAT, and it was assigned a “Low” quality rating in the PAGER inventory database. Figure 

14 shows the inventory information solicited from expert survey under the WHE-PAGER project 

that has been used to replace the inventory information of UN-HABITAT database. Various 

building type descriptions from expert judgment have been mapped to PAGER structure types. (MMud wall construction, C1- Concrete moment framed constructions, C2- RC shear-walled and dual 

framed constructions, C3- Concrete moment frame designed for gravity loads, UFB3- Unreinforced 

fire brick masonry with lime mortar and RM2- reinforced or confined masonry constructions). 

19
Inventory Compilation for Selected Countries 

This section provides several step-by-step examples of the procedure adopted for 

compilation of housing stock inventory for selected countries.

Australia 

Except for a few regions such as Newcastle/Lake Macquarie, most of Australia faces 

relatively low seismic hazards compared to more seismically active intra-plate regions of the world 

(Edwards, Robinson and others, 2004). However, in contrast to most stable continental regions, 

where the largest magnitude earthquakes (M > 6.0) are concentrated in failed rifts or passive 

margins, Australia’s largest onshore earthquakes have occurred mostly in the ancient Proterozoic 

and Archean terranes of western and central Australia (Johnston and Kanter, 1990). 

Information about nationwide building inventory in Australia is quite limited. However, a 

recently launched project by Geoscience Australia (Nadimpalli, Cornish and others, 2002) aims to 

develop a nationwide building exposure database compiling data from various agencies such as the 

Public Sector Mapping Agency (PSMA) and the Australian Bureau of Statistics (ABS). The 

housing type data from Australian Bureau of Statistics in 1986 compiled by (United Nations, 1993) 

indicated that about 76.7 percent of dwellings in Australia were separate homes, whereas 13.6 

percent were other medium-density dwellings, and 2.2 percent belonged to semi-detached houses. 

The information about construction characteristics was not available from any of the UN databases 

even in their recent compilations. 

We compiled the data from census of housing and population in their most recent census 

survey for Australia through written communication with Geoscience Australia (Edwards, written 

communication). The original housing census information is rigorous (by wall and roof/floor 

material) and has high spatial resolution with estimates of total dwellings by roof and wall 

construction material for each geographic region/census division of the country. However, we 

combined the regional distribution to country-level in the present inventory development effort 

using distribution of dwellings by construction material of the outer walls. The main construction 

materials described are double brick, brick veneer, timber, and fibro/asbestos cement, all of which 

have been mapped into the PAGER construction type categories (as shown in table 8) using 

engineering judgment. Our mapping is similar to the earlier mapping scheme suggested and used 

by Edwards, Robinson and others (2004) in their inventory development. 

El Salvador 

Most of the historical earthquakes in El Salvador originated along the Central American 

volcanic chain within the upper crust of the Caribbean plate (White and Harlow, 1987). At least 

dozen of earthquakes since 1710, mostly upper-crustal located in the San Salvador area, have 

caused damage in the city. 

The 1986 San Salvador earthquake (Mw 5.7) caused severe damage in the city of San 

Salvador (Harlow, White and others, 1993). It was estimated that 60 percent of the population of 

San Salvador (about 1,500,000) lived in marginal settlements and many of the structures had 

extremely poor earthquake resistance. The construction practice in the region had been strongly 

influenced by Spanish colonization and remained practically unchanged until the 1940s. Lara 

(1987) provide detailed descriptions of the construction practices in the San Salvador region. The 

construction type distribution compiled by INTERTECT after the earthquake indicates that about 

15 percent of the buildings were of wood; 10 percent made of block masonry; 37 percent 

20
constructed using traditional brick masonry construction; 37 percent of wood frame with poor infill 

walls (Bajaraque); and 1 percent of adobe construction (INTERTECT, 1986). According to Shiono 

and others (1991b), the survey conducted by INTERTECT was limited to established residential 

areas of the affected region, and extremely inferior dwellings were not included in the estimation. 

We have mapped the housing stock distribution using the (Shiono, Krimgold and others, 1991b) 

survey as shown in table 9. 

Indonesia 

Indonesia has experienced several catastrophic earthquakes including the 2004 Mw 9.0 and 

2005 Mw 8.7 Sumatra – Andaman Islands earthquakes, and the 2006 Mw 6.3 Yogyakarta earthquake 

in the recent past. The building stock in Indonesia performed extremely poorly during recent 

earthquakes in the country. A total of 156,662 housing units were totally destroyed whereas 

202,031 have suffered damage during the Yogyakarta earthquake in 2006 (BAPPENAS Report, 

2006). The four rural districts of Bantu, Klaten, Sleman, and Gunung Kidul suffered more than 91 

percent total housing destruction despite this moderate-sized (Mw 6.3) earthquake. According to the 

reconnaissance study carried out by the Mid-America Earthquake Center (Elnashai, Kim and 

others, 2007), the high level of building damage was mainly due to poor construction techniques, 

lack of seismic design provisions, and high population density in the region (1,600 persons/km2). 

Rural Indonesia is dominated by traditional one-story, unreinforced clay brick/block masonry in 

cement or lime mortar. The unreinforced clay brick, stone masonry walls support the timber roof 

system and transfer the load to random rubble stone strip or isolated footing. There is no adequate 

connection between timber roof frame and masonry walls. Most of the housing in urban areas in 

Indonesia is low to mid-rise reinforced concrete frame buildings with infilled masonry walls. These 

buildings have performed quite well and buildings of these types in Banda Aceh suffered only 

minor damage during the 2004 Indonesia earthquake (Saatcioglu, Ghobarah and others, 2006). The 

housing stock distribution compiled for PAGER inventory database is shown in table 10. 

Iran 

Iran has experienced many deadly earthquakes in the recent past which have resulted in 

huge casualties for example, 1962 Ms 7.3 Buyin-Zara; 1968 Ms 7.3 Dasht-e Bay z; 1972 Ms 6.9 

Ghir; 1976 Mw 7.0 Iran-Turkey border; 1978 Mw 7.3 Tabas; 1981 Mw 7.2 Sirch; 1990 Mw 7.4 Manjil; 

1997 Mw 7.2 Ardakul; and 2003 Mw 6.6 Bam. This highly seismic region forms the north-west 

trending boundary between the Arabian and Eurasian tectonic plates. The combination of high 

hazard and primarily poorly constructed adobe structures makes Iran an extremely high-risk 

country. UN data for Iran (1986) indicates that around 28.9 percent of housing units are adobe and 

more than 43 percent are made of clay brick masonry constructions (United Nations, 1993). 

Though this information is coming from the UN compilation for global housing, due to limited 

engineering input and the lack of professional engineering surveys in the development of these data 

requires us to assign a low quality rating to this data. 

A study conducted by Ghafory-Ashtiany and Mousavi (2005) after 2003 Bam earthquake 

indicated that about 84 percent of buildings (including 93.1 percent of adobe buildings) were built 

prior to the institution of seismic codes in Iran in 1991. After the Bam earthquake in 2003, around 

650 buildings were surveyed, primarily steel and reinforced concrete buildings that survived 

(Moghadam, 2005). The damage statistics show that about 65 percent of buildings were singlestory and most of the surviving buildings had masonry solid brick walls as partitions or bearing 

walls. As per the 2005 census (Iran Statistical Year Book, 2005), 10 percent of the dwellings have 

metal construction, 4 percent have reinforced concrete construction, 45 percent are brick and steel, 

21
14 percent are brick and wood, 6 percent are cement block and 20 percent are not stated (fig. 15). 

We used this data as a proxy representation of housing type distribution in both urban residential 

and nonresidential and rural nonresidential areas of Iran with “Medium” rating. According to 

Ghafory-Ashtiany and Mousavi (2005), about 40.6 percent buildings were made of brick and steel, 

1.9 percent were of brick and wood, 3.5 percent brick only, about 53.2 percent were of adobe (sun 

dried brick and clay) and 0.8 percent as concrete constructions. It is evident that large fraction of 

building types in Iran are non-engineering adobe or brick masonry construction. We used the 

inventory distribution suggested by Ghafory-Ashtiany and Mousavi (2005) for Bam and 

surrounding area as a proxy housing inventory especially in rural residential areas of Iran with a 

“High” rating as shown in table 11. 

Italy 

Primary tectonic activity in Italy results from the collision between the Eurasian and 

African plates. The high-topography zone of the central Apennines, which trends NW-SE within 

peninsular Italy, is dissected by a Quaternary fault system that has hosted several moderate to large 

historical earthquakes (Cello, Mazzoli and others, 1997); most recently the 1980 Mw 6.9 Iripinia; 

and the 1997 Mw 6.0 Umbria-Marche events. In 2002, the Molise and Puglia regions of southern 

Italy were struck by two moderate-magnitude earthquakes (Cara, Rovelli and others, 2005). The 

first and larger of the two (Mw 5.7) resulted in 30 casualties in the town of San Giuliano di Puglia. 

A scenario based damage study carried out by Faccioli, Pessina and others (1999) for residential 

buildings in the city area of Catania indicated that there are several limitations with the inventory 

data available from nationwide census of dwellings, or ISTAT (Italian Institute of Statistics). The 

authors used CONARI survey (a database which compiles information about buildings in the 

central city area of Catania) for their damage scenario study of Catania city (Faccioli, Pessina and 

others, 1999). This database was compiled based on a survey of buildings carried out by the 

Catania city planning office in late 1980s but it was limited to the central area of the city. The 

authors also discussed some results from an ongoing ad hoc local survey (also called LSU survey) 

of all buildings in the city. Recently Dolce and others (2006) conducted a vulnerability assessment 

and damage scenario study for Potenza (southern Italy) using the inventory data compiled from 

engineering surveys carried out after the 1990 earthquake that struck Potenza (the data were 

subsequently updated in 1999). The Potenza inventory indicates that about 62.1 percent of 

buildings are unreinforced masonry construction, about 36.3 percent are made of reinforced 

concrete framed buildings and 1.3 percent of other categories, but mainly steel construction. The 

authors classified this distribution in subsequent tiers of structural systems based on both volume of 

construction and the number of buildings. It is evident that the building stock in southern Italy, 

which is most prone to earthquakes, is an important dataset geographically for Italy and is used as a 

representative distribution for the whole of Italy for PAGER purposes. We have used the inventory 

distribution suggested for Potenza because this development was based on more detailed analysis 

of the inventory data by the authors, based on local construction and engineering design knowledge 

and also interpretations from other data sources and engineering surveys. We have mapped the 

distribution of building types by number into equivalent PAGER construction types using 

engineering judgment as shown in table 12. 

Japan 

Japan is a densely populated nation with high seismic hazard. Much of Japan’s population 

resides on thick sedimentary basins that have significant potential to amplify ground shaking. 

Tectonically, Japan is located near the junction of the Pacific, Philippine Sea and Okhotsk tectonic 

22
plates. The Pacific plate is moving northwest at a rate of about 8-9 cm/yr relative to, and 

subducting beneath, the Philippine Sea and Okhotsk plates. In addition, the Philippine Sea plate is 

subducting northward beneath Japan and the Okhotsk and Amurian plates (Miller and Kennett, 

2006). The complex tectonics drive the high rates of seismicity observed in the region, and the 

subducting slabs provide the source for the many active volcanoes that occupy the island. 

The housing and land survey conducted by Japan Bureau of Statistics is the most 

fundamental statistical survey on housing (Statistics Bureau Japan, 2003). The focus of the 2003 

Housing and Land Survey was dwellings, other occupied buildings, and all the households 

inhabiting those dwellings and buildings which were located in the enumeration unit districts at the 

time of the 2003 Housing and Land Survey. The data for Japan, Prefectures, and 14 major cities 

indicate that 31.7 percent of dwellings are of wooden constructions without fireproof protection, 

and 29.7 percent are wooden with fire protection. About 31.9 percent of the total dwellings are 

made of reinforced concrete constructions and 6.4 percent are of steel-framed construction. Most of 

the reinforced concrete-framed and steel-framed construction in Japan is relatively new and has 

been constructed after 1980. We have used the housing unit distribution available from Housing 

Census of 2003 for inventory compilation as shown in table 13. 

Mexico 

Mexico is situated on the boundary between large tectonic plates and is prone to frequent 

large earthquakes. It has a long history of destructive earthquakes. In September 1985, an 

earthquake measuring 8.1 and centered in the subduction zone off Acapulco killed more than 4,000 

people in the Mexico City, even though the city was located more than 300 km away from the 

epicenter of the event. 

The 1990 round of census data of population and housing in Mexico indicated that about 

69.9 percent of housing units were made of brick or stone block, 14.7 percent were made of adobe, 

8.2 percent of wood, and 7.2 percent of other materials. The most recent data from INEGI estimates 

about 75.7 percent of masonry construction (blocks, stone, or brick) and 24.3 percent of other wall 

materials. About 59.8 percent of these constructions had concrete or Tabique roofs, and 40.2 

percent had other nonclassified material, as shown in table 14. 

Nepal 

Geologically Nepal is situated on the boundary between the Indian and Tibetan plates. The 

Indian plate is continuously moving (subducting) under the Tibetan plate at a rate thought to be 

about 3 cm per year. The existence of the Himalayan range with the peak of Mount Everest is 

evidence of the continued tectonic activities beneath the country. Historically Nepal has witnessed 

several devastating earthquakes. The great Bihar-Nepal earthquake in 1934 was the most severe in 

recent times, killing more than 8,500 people and damaging approximately 80,000 homes. 

The data obtained from National Living Standards Survey (2003-04) indicates that about 

47.5 percent of households in Nepal are constructed using mud-bonded bricks or stones as the 

construction material for outer walls, whereas 18.5 percent are constructed using wood and 

approximately the same fractions have cement-bonded brick or stone walls with concrete roofs. 

There are around 15 percent households with largely informal materials; this fraction goes higher 

for the eastern part as compared to the western part of Nepal where wooden construction is more 

commonly used (Nepal Central Bureau of Statistics, 2004). About 32.3 percent of the housing units 

have straw or thatch roof; approx. 30 percent slate or tiled roof; 21 percent with galvanized iron 

(GI) sheet; and 13.6 percent concrete roof, as shown in table 15. 

23
New Zealand 

New Zealand straddles the boundary between the Indo-Australian and Pacific tectonic 

plates. The relative plate motion in New Zealand is expressed by a complex assemblage of active 

faults, coupled with a moderately high rate of seismicity (Stirling, McVerry and others, 2002). The 

plate boundary beneath New Zealand comprises the Hikurangi (North Island) and Fiordland (far 

south of South Island) subduction zones, which dip in the opposite sense relative to each other. 

Active tectonic motion is accommodated by the faults of the axial tectonic belt that traverse much 

of the South Island between the subducting slabs (Stirling, McVerry and others, 2002). The Alpine 

fault in the central South Island accommodates the release of much of the strain energy produced 

by the relative plate motion. 

Recently, a reconnaissance report following the Mw 6.5 1987 Edgecumbe, Bay of Plenty 

earthquake indicated that most of the damage occurred on drained swampland consisting of soft, 

unconsolidated soils (EQE, 1987). Based on these observations, the effects of site response are 

important to consider in hazard and risk assessments in regions such as New Zealand. 

The housing and population census data for New Zealand in the 2006 round indicate that 

58.4 percent of occupied dwellings in the country are separate single story houses, whereas 17.7 

percent are separate houses with two or more stories. About 10.2 percent of the dwellings have 

been classified as apartment or townhouses with two or more flats with single story, whereas only 

5.52 percent have two or more stories (New Zealand's Official Statistical Agency, 2006). The 

distribution of dwelling stock by use of construction material for the outer walls was inaccessible 

from the Web source. Our estimates for the housing stock distribution for New Zealand were 

primarily based on Dowrick (1998) and are shown in table 16. 

Taiwan 

The high seismicity in Taiwan results from the convergence of the Philippine Sea plate and 

the Eurasian plate. The Philippine plate is moving northwestward at a rate of about 7 cm/yr 

relative to the Eurasian plate. 

Although not abundant, mud brick residences are a special and traditional type of 

construction in Taiwan. This is mainly due to easily available, cost-effective construction material 

and weather conditions in the region, as this type of housing provides heat insulation (warm in 

winter and cool in summer) for the occupants. At the time of the magnitude 7.7, Chi-Chi 

earthquake (1999), though the number of mud-brick residences comprised only 5 percent of the 

total building stock in Taiwan, about 43.5 percent of the total casualties were attributed due to 

collapse of mud-brick houses (Tien, Juang and others, 2002). Another common building type in 

Taiwan urban areas is reinforced concrete (RC) buildings with infill masonry constructions. About 

18.6 percent of total victims died from the collapse of high rise (generally 10-15 stories) RC 

buildings, and 16.4 percent died in buildings which are shorter than 6 stories. The two most 

common housing types in urban areas are reinforced masonry buildings and RC buildings. Yao and 

Sheu (2002) and Tung and Yao (2002) provided more detail about such construction and its 

vulnerability to recent Taiwanese earthquakes. 

Although the information regarding total housing stock distribution in Taiwan exists in the 

form of householder registration data, it was not accessible to us for use in developing building 

inventory. Nevertheless, the housing stock distribution for most of the affected areas after the ChiChi earthquake is well documented and gives detailed information. We compiled the building stock 

distribution available for Nantau and Taichung County (Tien, Juang and others, 2002) and 

24
normalized them using population data. The housing type description has been mapped with 

PAGER structure type, and the estimated inventory distribution for Taiwan is shown in table 17. 

The Philippines 

The Philippine archipelago represents a complex system of micro-plates that are being 

compressed between two convergent plate margins that bound the nation; the Philippine Sea and 

Eurasian plates (EQE, 1990). Because of its tectonic setting, the Philippines experience frequent 

damaging seismic activity. The census of housing conducted by National Statistical Office of 

Philippines in the year 2000 round estimates 30.8 percent housing units of brick, stone or concrete 

walls whereas 22.7 percent are of wood among the occupied housing units in the Philippines. 

About 22.8 percent of the housing units in rural areas are of Bamboo/sawali/cogon/nipa, which is 

poor quality wood construction, and 18.9 percent of half wood and half concrete/brick/stone 

category. We mapped the construction material type to PAGER structure type and developed 

fraction of housing units by construction type as shown in table 18. 

Turkey 

Turkey is situated in one of the most seismically active regions of the world with a large 

part of the country at significant risk of major earthquake catastrophe. The region is tectonically 

active and experiences frequent destructive earthquakes. First-order tectonics is controlled by the 

collision of the Arabian plate and the Eurasian plate. A large piece of continental crust almost the 

size of Turkey, called the Anatolian block, is being compressed in a westerly direction. The block 

is bounded on the north by the North Anatolian fault and to the south east by the East Anatolian 

fault. Several region-specific efforts have been made by the researchers in Turkey to understand the 

building stock inventory and its vulnerability in the past. The assessment of proportion of buildings 

in each structure type category and its occupancy were based on several localized surveys after 

recent large earthquakes, either in post-earthquake damage or vulnerability studies (Bogaziçi 

University, 1992; EEFIT, 1993; Erdik and Aydinoglu, 2002). Bommer, Spence and others (2002) 

recently developed the earthquake risk model for Turkey under the Turkish Emergency Flood and 

Earthquake Recovery Program (TEFER) project. The development of inventory is based on 

classification of construction types, which in turn is based on vulnerability as observed during 

recent earthquakes. The authors also provided typical distribution of residential building stock 

assumed for some locations. Though the authors have not clearly indicated the location name or its 

spatial coverage, we believed that the distribution is generally indicative of overall rural and urban 

Turkish building stock (fig. 16). We mapped the original classification (both urban as well as rural) 

to PAGER structure type classification and used these most recent data for PAGER inventory 

development purposes (table 19). 

Application to the Continental United States 

Several earthquake-specific research studies conducted in the United States specifically for 

California have provided new directions in global earthquake hazard and risk understanding. ATC13 (ATC., 1985) developed the first broad inventory database for California by compiling 

databases from several different sources. Later, FEMA (FEMA, 2006) adopted the inventory 

development methodology of ATC-13 and extended the effort to the rest of the United States for 

use in FEMA’s earthquake loss estimation software HAZUS (Hazards U.S.). We adopted the 

inventory developed within the framework of HAZUS for the PAGER inventory database because 

25
it was developed through a rigorous, nationwide effort involving several different databases across 

the country. HAZUS’s default building stock data cover residential, commercial, industrial, 

agricultural, religious, government, and educational buildings. The building inventory classification 

system developed for HAZUS consists of a two-dimensional matrix relating model building types 

grouped in terms of structural system to building occupancy classes. The PAGER regionalization 

scheme adopted for global earthquake casualty estimation classifies the United States into two 

broad regions, namely California and the rest of the United States (Porter and Jaiswal, 2006 

unpublished report). 

The ATC-13 project was one of the most rigorous efforts to compile the entire facility 

inventory for California. It was based on calculation of a matrix that relates Social Function 

Classification and Earthquake Engineering Facility Classification for each postal ZIP code in 

California. The element of a matrix associated with each ZIP code gives the percentage of the total 

building floor area derived from nine characteristics matrices, each of which was considered valid 

for a given combination of building ages (pre-1950, 1950-1970 and post 1970) and number of 

stories (low rise, 1-3 stories; medium rise, 4-7 stories; and high rise, 8+ stories). The primary 

database used for residential building inventory development was United States Decennial 

Population Census database, and a high-rise building database covering about 3,000 buildings in 

California (both residential and nonresidential) was derived from several individual data sources. 

The HAZUS facility specific inventory development for California was based on similar 

principles. However, the HAZUS developers subsequently made alterations to the original ATC-13 

classification scheme to reflect more updated inventory information (Kircher, Whitman and others, 

2006). Some of the key changes associated with residential buildings include assigning all the 

single family residential housing in California to be 99 percent wood and 1 percent reinforced 

masonry constructions. The multi-family housing (RES3) replaced the 5 percent of Mobile Homes 

(MH) with wood (W1) category and other modifications made in terms of code level assignments. 

As the HAZUS inventory database was developed after rigorous State and national level efforts, we 

used the default HAZUS building inventory data compiled for California directly in our PAGER 

inventory development as shown in table 20. 

As the building inventory was provided at the Census tract level, we combined the total 

building stock distribution first at the State level and then calculated a combined estimate for all the 

States (except California) to estimate the inventory for the rest of the United States. 

Web Delivery of Global Inventory 

A global housing database is practically nonexistent except for a few data sources that 

provide limited and generally inconsistent information about housing types and (or) their 

distribution. The inventory database developed in this report therefore provides a step forward not 

only to motivate the development of more refined databases with higher resolution but also to use 

more consistent approaches for their development. It is evident that the future housing data 

compilations should be based on uniform methodologies and nomenclature that can easily 

accommodate engineering-based rather than indigenous terms (which are often confusing and do 

not provide adequate information about their structural systems). This is especially important in 

cases where engineering professionals are not involved in the data compilation. In cases where the 

data compilation is based on engineering surveys, the definition of structure type should be 

consistent with international standards for the definition of construction types. 

26
We plan to provide this database online in the form of electronic supplement along with this 

report so that engineers and professionals from individual countries or international experts can 

view the data online and can suggest appropriate modifications based on the latest research 

conducted or expert opinions in their respective countries. The feedback received from respective 

country experts in terms of new data or modifications to the original data would be utilized to 

upgrade the default PAGER inventory database. 

Implementation Challenges 

Processing of Raw Data 

One of the key challenges we faced during processing of raw data available from different 

sources was the variable format in which they were available. Most of the data were available in 

either paper or digital form but variable formats such as HTML, spreadsheets, published literature 

in scanned PDF (Adobe) reports were also common. Hence significant manual effort was required 

to bring these into digital format. Raw data have been processed into an appropriate form using a 

standard attribute mapping scheme specific to the data source, as discussed in previous sections. 

Construction Type Mapping 

We have observed that the nomenclature used for the description of construction types 

varies for different data sources and often varies from country to country. As most of the housing 

survey exercises are not conducted by engineering professionals, the descriptions of construction 

types from the census need to be used with caution. A typical case is when material used for 

construction of the building façade is interpreted as wall material, leading to an incorrect 

representation of construction types for that building. However, it is not possible to correct this in 

the basic data that are available for that country. Essentially, we need better mapping by local 

experts to either collate or segregate the original class of housing types into PAGER defined or 

other standard construction type definitions. As discussed in previous sections, we conducted 

limited Internet-based, country-specific research and reviewed the published literature for examples 

to identify common building types and construction practices; however, this approach could be 

further improved with additional information sources. 

Occupancy by Construction Type 

PAGER requires structure-specific occupancy information to estimate the likely casualties 

due to structural collapse or damage. The WHE database (EERI, 2007) is the only publicly 

available source that provides occupancy pattern by construction type (for some countries) as 

shown in Appendix V. Note also that average occupancy varies with time of day, day of week, and 

by type of occupancy (single or multi-family residential, commercial, or industrial). However, as 

discussed in the previous section, the PAGER loss methodology applies time of day corrections to 

estimate likely population occupying either the residential or nonresidential category. Hence, the 

likely average occupancy in each of these two broad categories needs to be estimated for each 

geographic region (country level). 

27
Confidence Rating 

Owing to large variations in the quality of data and types of data sources, it was important 

to rate the data sources based on unified approach in order to pick the right inventory information 

among several available sources. Confidence rating was solely based on the procedure adopted for 

collection of the data (ordinary versus engineering survey), purpose of the inventory data, and 

involvement of engineering expertise in the raw data development. 

As discussed in the preceding sections, mapping of inventory distribution from one country 

to its neighboring country where data are not available entails additional uncertainties. Since such 

mapping has been carried out as a last measure and was based on limited research, we have 

therefore assigned “Low” rating to such mapping. 

Limitations of PAGER Inventory Database 

Housing Units and Building/Structure Type Inventory 

A single building may have one or multiple housing units (single family dwelling vs. multifamily apartment buildings common in urban areas); hence the associated structure type 

distribution may change when we consider distribution of housing type against distribution of 

buildings for a certain country. Here we refer to the datasets that provide construction type data for 

housing units rather than of buildings. It includes most of the data originating from Housing Census 

surveys, the UN-HABITAT database, UN compilations (1993) and the EUROSTAT database. The 

dataset which provides structure type distribution directly for buildings are engineering surveys, 

world housing encyclopedia (WHE) database (EERI, 2007), published literature e.g., previous loss 

estimation studies in the region, loss modeling software (e.g., HAZUS in U.S., QUAKELOSS in 

Russian Federation, KOERIloss in Turkey, EQRM in Australia). 

In order to have uniformity in the data development, we assign a unique identifier for the 

database that directly provides information about whether the dataset has building-specific 

distribution instead of housing unit distribution. We then estimate the likely PAGER structure type 

distribution corresponding to housing units based on the approximate number of units estimated 

from Appendix VI for each PAGER structure type. This estimation of approximate number of 

housing units by PAGER structure type is based on the World Housing Encyclopedia database. It 

represents the single largest compilation of housing data for more than 35 countries and is 

compiled by engineering and architectural professionals. Statistical extrapolation of such data 

beyond the specific country may or may not be very accurate. We believed such approximation to 

be necessary in the absence of any data, and the errors in such extrapolation may be of second 

order in terms of its impact on likely casualty estimation. However, future updates of the inventory 

database can help reduce such uncertainties in the inventory distribution. 

Wall Material and Structure Type 

The inventory database developed in this investigation is based on mapping wall material to 

PAGER structure types. Care has been given to follow the same mapping conventions across 

different data sources for similar descriptions. During mapping of the wall material descriptions, no 

subjective judgments were made to alter the original input data (in terms of fractions of units of 

certain wall material) to multiple structure types. Additional inputs from local engineering 

28
professional or loss modeling experts in a particular country may have provided better judgment in 

assigning one wall material type to certain structure types. 

Vintage of Database 

It is evident from the UN (1993) Housing Census data that for a few countries, the housing 

data compilation is much older than for the rest of the countries. Tremendous population growth 

followed by rapid urbanization in certain countries has influenced construction standards and 

practices. Similarly, seismic activity in a region can change the historical construction practice due 

to building code requirements. For example, URM or adobe constructions in certain countries are 

either significantly retrofitted or completely banned; substantial improvements in building code 

provision and its enforcement takes place in certain countries. However, the housing data do not 

reflect such changes, which can affect not only the inventory distribution but also the relative 

vulnerability of building stock and PAGER casualty estimates. It is extremely important that the 

inventory database developed herein be constantly updated and improved with input from local 

engineering professionals. 

Resolution of Inventory Distribution 

Except for a few cases, most of the risk or loss estimation exercises conducted around the 

world largely focus on “regional-level” risk or loss assessment. It can be in the form of a scenario 

of a major earthquake around a known seismogenic zone such as, Vrancea in Romania, Marmara in 

Turkey, New Madrid in the Central United States or to focus around a group of important cities or 

a city such as, Basel in Switzerland, Istanbul in Turkey, Tokyo in Japan, San Francisco in the 

United States, and Catania in Italy. The inventory database developed during such efforts are 

mostly applicable to smaller geographic regions and are not necessarily applicable to a country as a 

whole. Even though higher resolution inventory distribution for better regional risk estimation is 

extremely important, only a handful of countries have such information publicly available. At 

present, due to lack of data, the minimum geographic resolution selected for PAGER inventory 

development is at the country level except in the U.S., where California and the rest of the U.S. are 

treated as separate regions. In cases of countries where the inventory distribution is known only for 

certain geographic location instead of country as a whole, we have considered the same distribution 

to be valid for the entire country. Future inventory development for PAGER warrants much higher 

resolution beyond the existing country-level distribution in order to incorporate regional variations 

in the loss estimation (highly developed urban area of a country vs. remote, less developed area; 

seismically prone areas within a country or large-sized countries with high geological variation). 

Scope of Inventory Updating 

The inventory developed within the framework of the PAGER inventory requirements 

allows easy updating of country/region-specific inventory information based on the quality of 

inventory data and its vintage. The present investigation suggests that for many countries, the 

inventory information is extremely limited or nonexistent. However, we believe that future research 

studies or data compilation efforts would fill such gaps and would also help in improving the global 

inventory data compilation for the PAGER project. One such effort is envisioned through a joint 

collaborative project between USGS and EERI’s World Housing Encyclopedia (WHE). The WHEPAGER project is an ongoing collaborative effort initiated by a group of experts from World 

Housing Encyclopedia (WHE) and U.S. Geological Survey’s PAGER project for better estimation 

of building inventory and building vulnerability worldwide (Porter, Greene and others, 2008). Note 

29
that the existing online WHE report database (EERI, 2007) covers only residential building types 

and does not cover information specific to (1) nonresidential building types, (2) fraction of building 

types in rural or urban areas, (3) vulnerability characteristics, and (4) occupancy characteristics 

(day and night occupancy pattern). The WHE-PAGER project provides a framework for 

compilation of this information in a simple and consistent format solicited mainly from earthquake 

engineering professionals from different countries. The data compiled in this exercise will also help 

in enhancing the housing stock distribution and vulnerability data for the existing WHE housing 

report for different countries. 

Country-specific experts have been requested to provide their best estimate of the 

predominant building types in their country, the probability of collapse as a function of intensity for 

these building types, and the fraction of the urban and rural population who live and work in each 

building type. These estimates are only first-order approximations, and they are not meant to 

substitute for the more sophisticated modeling and analysis work that is taking place in some 

countries. Rather, the estimates provided here are meant to complement such efforts, and to be a 

first step in promoting the need for development of a rigorous database throughout the world. 

Similarly, the inventory-specific information compiled in the WHE-PAGER project provides a 

broad occupancy pattern of human exposure in urban, rural, residential or nonresidential dwellings 

by its construction types. It does not directly represent the fraction of dwellings by its construction 

types as obtained from other data sources during the housing inventory compilation effort of this 

report. However, PAGER casualty loss modeling efforts mainly require human exposure 

distribution by its construction types. 

Housing type distribution by its construction material (given by most of the data sources 

such as Housing Censuses, UN-compilations or published reports) for a country is similar to human 

exposure distribution by construction types as long as average occupancy (number of people) per 

construction type does not change. It has been assumed that fraction of dwellings by construction 

types as compiled during this report is generally similar to fraction of population occupied by 

certain construction types as obtained in WHE-PAGER survey. The distribution of population 

exposure by time of day and by type of occupancy for a given density of population (urban or rural 

environment) as required for earthquake casualty loss modeling is discussed in detail by Jaiswal 

and Wald (work in progress). 

Within short span, the WHE-PAGER survey resulted in a development of comprehensive 

inventory-specific information for 23 countries in a standardized format covering various aspects of 

human occupancy pattern, construction types and vulnerability for individual countries 

(www.world-housing.net). Table 21 shows a response obtained from expert from Japan which 

indicates various construction types common in Japan and the occupancy pattern associated with 

each construction type both in residential and nonresidential units. The growing responses of 

professionals especially from earthquake prone countries have clearly demonstrated the need of 

such an open knowledge-sharing environment, which can help not only to identify elements at risk 

but also to develop indigenous earthquake risk mitigation measures to minimize the consequences. 

This report includes the inventory-specific information solicited from country-specific expert 

during the first phase of WHE-PAGER inventory database and is provided in an electronic 

supplement (Appendix VII) of this report. 

Summary 

The framework proposed for PAGER inventory development in this investigation utilizes 

several nonproprietary and openly available global data sources and collates them using certain 

30
predefined criteria on quality and vintage and at the required geographic resolution (presently at 

country level, though each sub region/polygon within a particular country could be treated in the 

same way). This framework provides a globally consistent approach for treating a variety of 

diverse datasets obtained from sources with varying levels of uncertainty, quantity, and quality. 

The investigation results in synthesis of different data sources of residential housing types with 

variable quality to produce a single, consistent global inventory dataset for predicting social and 

economic impacts of global earthquakes. 

Even though better quality building data exist for various countries, much of it is 

proprietary, being available to either insurance or re-insurance agencies, country-specific research 

institutions, or private industry. Often, such data are focused on insured (and likely engineered) 

properties and thus would not be useful for overall country-wide inventory and impact assessment. 

This investigation demonstrates that despite the substantial efforts that have been made over recent 

years by census agencies around the world, several limitations must be overcome before housing 

census survey data can be directly utilized for global earthquake loss estimation and mitigation. 

This report also points out that due to limited and poor quality of some of the datasets for certain 

hazard-prone countries which later were indicated by “Low” ratings, it is essential that we, 

collectively, identify suitable cost-effective options for compiling such information. The WHEPAGER project demonstrates one such alternative for compiling an open, collaborative, peerreviewed data for PAGER inventory development as well as other uses. 

In the present investigation, we considered country-level aggregate distributions of housing 

units based on construction type for global earthquake casualty estimation. This approach was 

warranted by the general lack of data availability beyond this resolution globally. Nevertheless, 

once finer resolution data become available, the present framework can easily utilize them by 

creating additional subregions within a country to estimate appropriate building inventory 

classification. Likewise, results from our general approach stand as a framework and global default 

“starting model,” which, we readily concede, portions of which will be replaced with higher quality 

and higher resolution data as they become available. 

The framework of present inventory development methodology is not entirely new and is in 

many ways analogous to the ATC-13 (ATC., 1985) and HAZUS (FEMA, 2006) inventory 

development methodologies. Here, we have adapted a similar approach for use on a global scale. 

Although this approach has already been available for more than 20 years, it has been utilized for 

recent inventory development procedures as in HAZUS. We used similar procedures, although we 

modified ours to suit our global inventory development requirements. In particular, we utilized a 

variety of data quality levels from a range of different types of data sources in self-consistent ways 

(in terms of vintage and quality ratings) before assigning inventory distribution for individual 

country. As the ratings and vintage information are available in the process of selecting the best 

data for a county, future updates could be easily made specific to particular source of data based on 

more recent information for that region or country. Even though the present framework is simple 

and easy to modify, it is largely untested, and it is therefore likely that future modifications may be 

required within the compiled database to improve the original data quality or to review specific 

data sources based on sampling and ground-truth observations. 

Conclusions 

The methodology described in this report provides a simple, cost-effective approach for 

global, country-wide, residential building inventory data compilation across various data sources 

with variable data quality and vintages. The main purpose of the inventory compilation is for 

31
earthquake loss assessment and risk management. The methodology consists of identification of 

data source, attribute mapping, quality assessment and rating, synthesis of data, and, where 

necessary, the assignment of inventory distribution to the countries with no data. The report 

identifies various key data sources that provide building inventory information at a global scale and 

develops a unique building attribute mapping scheme for each of the data sources to map the raw 

data into uniform PAGER structure types. The mapping scheme developed in the present report has 

been specifically designed with the expectation of future enhancements from new inventory data 

sources. For many countries, the building inventory and vulnerability data were clearly either 

completely missing, patchy, or could not be known during the phase of this investigation. However, 

development of country-pairing scheme to identify the “best neighbor” depending on data quality 

and vintage has helped in the development of reasonable default inventory distribution for such 

countries. Such a country pairing was based purely on expert judgment and will suffice until better 

quality data become available. An ongoing collaborative effort in the form of the WHE-PAGER 

project during its second phase will help to replace country-pairing based data synthesis and will 

result in better estimation of building inventory and building vulnerability worldwide. This report 

summarizes the procedure adopted for housing stock data development for more than 200 countries 

(fig. 17) and provides detailed discussion on inventory data development for a few example 

countries. Although the database development exercise was extremely laborious and timeconsuming due to inherent limitations in the raw data, we have made a sincere effort to develop a 

building inventory distribution database for all the residential building stock at a global scale within 

a short span of 6 months, with extremely limited resources. 

The report (along with its electronic supplement) also discusses the nature of available 

inventory data and its limitations, develops a framework of future inventory compilation using the 

source specific mapping scheme, and seeks various avenues for better inventory data compilations 

at global scale which can be utilized for PAGER and other loss estimation needs. Future inventory 

updating or adoption of more recent information (such as, satellite-based inventory data 

compilation, the availability of region-specific or more recent loss-centric inventory data especially 

covering financial aspects of losses) could easily replace the default estimation present in the 

database and enhance the overall quality of this global inventory data. Because the attribute 

mapping has been applied globally to all the data and is based on limited understanding in terms of 

regional construction practice and accessibility of published reports or research articles or Internet 

research, future updating will be required for many countries as specific information becomes 

available via the scientific community. This investigation also demonstrates that there is a strong 

need to seek an exercise which can encourages open data development and sharing mechanisms at 

global scale in order to continue improving the global building inventory data especially of 

nonresidential buildings and to seek various avenues for improving the raw inventory data 

compilation efforts at local levels by adopting globally consistent data formats that can be used for 

PAGER and other loss estimation needs. 

Acknowledgments 

We thank Keith Porter for reviewing the mapping schemes used in the present investigation 

and also for many useful suggestions that helped to improve the manuscript. Mboup Gora of United 

Nation’s UN-HABITAT provided demographic and health survey data. 

We also thank Paul Earle, Robin Spence, and Trevor Allen for useful discussions during the 

phase of this investigation. Special thanks to Kristin Marano and Tim Taylor for helping in data 

preparation for several of the data sources. 

32
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39
United Nations, 2001, Compendium of Human Settlements Statistics 2001: United Nations, New 

York, 246 p. 

United Nations, 2005, List of countries that conducted population and housing census (1985-2004). 

Wald, D.J., Earle, P.S., Lin, K., Quitoriano, V., and Worden, B.C., 2006, Challenges in rapid 

ground motion estimation for the prompt assessment of global urban earthquakes: Bulletin 

of Earthquake Research Institute, v. 81, p. 275-283. 

Wald, D.J., Worden, B.C., Quitoriano, V., and Pankow, K.L., 2005, ShakeMap manual -- technical 

manual, user's guide, and software guide: U.S. Geological Survey, 132 p. 

White, R.A., and Harlow, D.H., 1987, The San Salvador earthquake of October 10, 1986 - 

seismological aspects and other recent local seismicity: Earthquake Spectra, v. 3, no. 3, p. 

419-434. 

Yao, G.C., and Sheu, M.S., 2002, Street front building with arcade at the first floor (contemporary 

construction) World Housing Encyclopedia Report, no. Sept 2007. 

40
Glossary 

Building Inventory Building stock distribution according to its structure types.

Construction/Structure Type An assembly of one or several units to create a space for living 

or shelter. A structure type is defined in general based on several factors, including structural 

material used in construction, horizontal and vertical load resisting system, or performance of 

structural system during earthquake ground shaking. 

Dwelling A structure, part of a structure, or group of structures that is used, or intended to be 

used as a place where people reside. A dwelling may be permanent or temporary and may function 

as private or non-private. This term is commonly used in defining construction characteristics for 

Population and Housing Census surveys. 

EERI Earthquake Engineering Research Institute

Household One person usually living alone, or two or more people usually living together and 

sharing facilities (such as, eating facilities, cooking facilities, bathroom and toilet facilities, a living 

area), in a private dwelling. This term is commonly used in conducting Population and Housing 

Census surveys.

PAGER Prompt Assessment of Global Earthquakes for Response 

WHE World Housing Encyclopedia

41
Appendix I 

PAGER structure type categories used for global building inventory development. 

Label Description (according to construction/structure type) Average No. 

of stories 

Typical 

W WOOD 1-3 2 

W1 Wood Frame, Wood Stud, Wood, Stucco, or Brick Veneer 1-2 1 

W2 Wood Frame, Heavy Members, Diagonals or Bamboo Lattice, 

Mud Infill 

All 1 

W3 Wood Frame, Prefabricated Steel Stud Panels, Wood or Stucco 

Exterior Walls 

2-3 2 

W4 Log building 1-2 1 

S STEEL All 1 

S1 Steel Moment Frame All 1 

S1L Low-Rise 1-3 2 

S1M Mid-Rise 4-7 5 

S1H High-Rise 8+ 13 

S2 Steel Braced Frame All 1 

S2L Low-Rise 1-3 2 

S2M Mid-Rise 4-7 5 

S2H High-Rise 8+ 13 

S3 Steel Light Frame All 1 

S4 Steel Frame with Cast-in-Place Concrete Shear Walls All 1 

S4L Low-Rise 1-3 2 

S4M Mid-Rise 4-7 5 

S4H High-Rise 8+ 13 

S5 Steel Frame with Un-reinforced Masonry Infill Walls All 1 

S5L Low-Rise 1-3 2 

S5M Mid-Rise 4-7 5 

S5H High-Rise 8+ 13 

C REINFORCED CONCRETE All 1 

C1 Ductile Reinforced Concrete Moment Frame All 1 

C1L Low-Rise 1-3 2 

C1M Mid-Rise 4-7 5 

C1H High-Rise 8+ 13 

C2 Reinforced Concrete Shear Walls All 1 

42
C2L Low-Rise 1-3 2 

C2M Mid-Rise 4-7 5 

C2H High-Rise 8+ 13 

C3 Nonductile Reinforced Concrete Frame with Masonry Infill 

Walls 

All 1 

C3L Low-Rise 1-3 2 

C3M Mid-Rise 4-7 5 

C3H High-Rise 8+ 13 

C4 Nonductile Reinforced Concrete Frame without Masonry Infill 

Walls 

All 1 

C4L Low-Rise 1-3 2 

C4M Mid-Rise 4-7 5 

C4H High-Rise 8+ 13 

C5 Steel Reinforced Concrete (Steel Members Encased in 

Reinforced Concrete) 

All 1 

C5L Low-Rise 1-3 2 

C5M Mid-Rise 4-7 5 

C5H High-Rise 8+ 13 

PC1 Precast Concrete Tilt-Up Walls All 1 

PC2 Precast Concrete Frames with Concrete Shear Walls All 1 

PC2L Low-Rise 1-3 2 

PC2M Mid-Rise 4-7 5 

PC2H High-Rise 8+ 13 

RM REINFORCED MASONRY All 1 

RM1 Reinforced Masonry Bearing Walls with Wood or Metal Deck 

Diaphragms 

All 1 

RM1L Low-Rise 1-3 2 

RM1M Mid-Rise (4+ stories) 4-7 5 

RM2 Reinforced Masonry Bearing Walls with Concrete Diaphragms All 1 

RM2L Low-Rise 1-3 2 

RM2M Mid-Rise 4-7 5 

RM2H High-Rise 8+ 13 

MH Mobile Homes All 1 

M MUD WALLS 1 1 

M1 Mud Walls without Horizontal Wood Elements 1-2 1 

M2 Mud Walls with Horizontal Wood Elements 1-3 2 

A ADOBE BLOCK (UNBAKED DRIED MUD BLOCK) 

WALLS 

1-2 1 

43
A1 Adobe Block, Mud Mortar, Wood Roof and Floors 1-2 1 

A2 Same as A1, Bamboo, Straw, and Thatch Roof 1-2 1 

A3 Same as A1, Cement-Sand Mortar 1-3 2 

A4 Same as A1, Reinforced Concrete Bond Beam, Cane and Mud 

Roof 

1-3 2 

A5 Same as A1, with Bamboo or Rope Reinforcement 1-2 1 

RE RAMMED EARTH/PNEUMATICALLY IMPACTED 

STABILIZED EARTH 

1-2 1 

RS RUBBLE STONE (FIELD STONE) MASONRY All 1 

RS1 Local Field Stones Dry Stacked (No Mortar). Timber Floors. 

Timber, Earth, or Metal Roof. 

1-2 1 

RS2 Same as RS1 with Mud Mortar. 1-2 1 

RS3 Same as RS1 with Lime Mortar. 1-3 2 

RS4 Same as RS1 with Cement Mortar, Vaulted Brick Roof and 

Floors 

1-3 2 

RS5 Same as RS1 with Cement Mortar and Reinforced Concrete 

Bond Beam. 

1-3 2 

DS RECTANGULAR CUT STONE MASONRY BLOCK All 1 

DS1 Rectangular Cut Stone Masonry Block with Mud Mortar, 

Timber Roof and Floors 

1-2 1 

DS2 Same as DS1 with Lime Mortar 1-3 2 

DS3 Same as DS1 with Cement Mortar 1-3 2 

DS4 Same as DS2 with Reinforced Concrete Floors and Roof 1-3 2 

UFB UNREINFORCED FIRED BRICK MASONRY All 1 

UFB1 Unreinforced Brick Masonry in Mud Mortar without Timber 

Posts 

1-2 1 

UFB2 Unreinforced Brick Masonry in Mud Mortar with Timber 

Posts 

1-2 1 

UFB3 Unreinforced Fired Brick Masonry, Cement Mortar, Timber 

Flooring, Timber or Steel Beams and Columns, Tie Courses 

(Bricks Aligned Perpendicular to the Plane of the Wall) 

1-3 2 

UFB4 Same as UFB3, but with Reinforced Concrete Floor and Roof 

Slabs 

1-3 2 

UCB UNREINFORCED CONCRETE BLOCK MASONRY, 

LIME/CEMENT MORTAR 

All 1 

MS MASSIVE STONE MASONRY IN LIME/CEMENT 

MORTAR 

All 1 

TU PRECAST CONCRETE TILT-UP WALLS (same as 

HAZUS Type PC1 in Developing and Undeveloped Countries)

All 1 

INF INFORMAL CONSTRUCTIONS (parts of Slums/Squatters) 

Constructions Made of Wood/Plastic Sheets/Galvanized Iron 

sheets/Light Metal or Composite etc., not Confirming to 

Engineering Standards.

All 1 

UNK Unknown Category (Not specified) All 1 

Note: All-refers to all the possible ranges of number of stories of a particular structure type. 

44
Appendix II 

Average occupancy per dwelling type compiled from United Nations data (UNECE, 

2006). 

Serial No. Country Year Total persons/housing unit 

Total Urban Rural 

1 Albania 2001 3.9 3.9* 3.9* 

2 Algeria 2006 6.1 6.1* 6.1* 

3 Argentina 1991 3.8 3.8 4.1 

4 Austria 1997 2.5 2.3 3.1 

5 Azerbaijan 1998 4.9 4.4 5.8 

6 Bahamas 1990 4.1 4.2 3.6 

7 Bangladesh 2006 5.9 5.9* 5.9* 

8 Belgium 1991 2.5 2.5 2.8 

9 Bermuda 1991 2.6 2.6 2.5 

10 Brazil 1998 3.8 3.7 4.1 

11 Bulgaria 1992 3.0 3.1 2.9 

12 Burkina Faso 1991 6.6 6.4 6.6 

13 Cambodia 2006 6.2 6.2* 6.2* 

14 Canada 1996 2.6 2.6 3.2 

15 Channel Islands Guernsey 1996 2.5 2.5* 2.5* 

16 Columbia 1993 4.5 4.3 5.0 

17 Congo-Brazzaville 2006 8.1 8.1* 8.1* 

18 Costa Rica 1997 4.2 4.0 4.3 

19 Croatia 1991 3.2 3.1 3.4 

20 Cyprus 1992 3.2 3.2 3.3 

21 Czech Republic 1991 2.8 2.7 2.9 

22 Denmark 2003 2.1 2.1* 2.1* 

23 Egypt 1996 4.6 4.3 5.0 

24 Estonia 2003 2.2 2.2* 2.2* 

25 Finland 1998 2.3 2.9 2.5 

26 France 1990 2.6 2.5 2.8 

27 French Polynesia 2006 5.8 5.8* 5.8* 

28 Gabon 2006 6.3 6.3* 6.3* 

29 Gambia 1993 8.9 7.0 10.5 

30 Greece 2001 2.0 2.0* 2.0* 

31 Guam 1990 4.0 3.7 4.1 

32 Guatemala 1994 5.3 5.0 5.5 

33 Guinea 2006 6.3 6.3* 6.3* 

34 Guinea- Bissau 2006 5.6 5.6* 5.6* 

35 Hong Kong 1996 3.5 3.5* 3.5* 

36 Hungary 1990 2.7 2.7 2.9 

37 Iceland 1993 2.7 2.7* 2.7* 

38 Ireland 2002 2.8 2.8* 2.8* 

39 Jordan 2006 5.7 5.7* 5.7* 

40 Korea 1995 4.7 5.2 3.5 

41 Kuwait 2006 6.4 6.4* 6.4* 

42 Kyrgyzstan 2006 5.7 5.7* 5.7* 

43 Latvia 2003 2.4 2.4* 2.4* 

44 Lesotho 1996 5.0 5.0* 5.0* 

45
45 Liechtenstein 2000 2.3 2.3* 2.3* 

46 Luxembourg 2001 2.6 2.6* 2.6* 

47 Macau 1996 3.5 3.5* 3.5* 

48 Macedonia 2002 2.9 2.9* 2.9* 

49 Malaysia 1991 4.9 4.5 5.3 

50 Malta 1983 3.0 3.0* 3.0* 

51 Mauritius 1990 4.9 4.5 5.1 

52 Mexico 1995 4.7 4.5 5.1 

53 Moldova 2002 2.8 2.8* 2.8* 

54 Montenegro 2002 2.4 2.4* 2.4* 

55 Netherlands 1998 2.3 2.2 2.5 

56 Nicaragua 1995 5.8 5.5 6.1 

57 Norway 1990 2.4 2.3 2.6 

58 Pakistan 2006 7.2 7.2* 7.2* 

59 Panama 1990 4.4 4.2 4.7 

60 Pitcairn 1999 2.9* 2.9* 2.9 

61 Poland 1995 3.5 3.2 4.1 

62 Portugal 1991 3.2 3.1 3.3 

63 Puerto Rico 1990 3.3 3.2 3.5 

64 Republic of Moldova 2003 2.8 2.8* 2.8* 

65 Romania 2003 2.7 2.7* 2.7* 

66 Sabah 1991 5.5* 5.5 5.5* 

67 Saudi Arabia 2006 5.9 5.9* 5.9* 

68 Serbia 2002 2.5 2.5* 2.5* 

69 Serbia and Montenegro 1999 3.3 3.3* 3.3* 

70 Slovakia 1991 3.3 3.1 3.5 

71 Slovenia 2003 2.5 2.5* 2.5* 

72 St. Helena 1998 3.1 3.1* 3.1* 

73 Sudan 2006 6.1 6.1* 6.1* 

74 Sweden 1990 2.1 2.1 2.4 

75 Switzerland 1990 2.4 2.3 2.6 

76 Syrian Arab Republic 1994 6.7 6.3 7.1 

77 Tonga 1996 6.0 6.1 6.0 

78 Turkey 1994 4.4 4.2 4.7 

79 United Arab Emirates 2006 6.4 6.4* 6.4* 

80 United Kingdom 1996 2.4 2.4 2.5 

81 United States 1997 2.6 2.6 2.7 

82 Uruguay 1996 3.3 3.3 3.3 

83 Uzbekistan 2006 5.8 5.8* 5.8* 

84 Yugoslavia 1991 3.2 3.1 3.3 

* interpreted data where specific estimates are unavailable. 

46
Appendix III 

Estimated work (labor) force population in different countries. 

Serial 

No. Country 

Work 

(Labor) 

Force 

Population Fraction Year 

1 Afghanistan 15000000 31889923 47.0% 2007 

2 Albania 1090000 3600523 30.3% 2007 

3 Algeria 9310000 33333216 27.9% 2007 

4 American Samoa 17630 57663 30.6% 2007 

5 Andorra 42420 71822 59.1% 2007 

6 Angola 6393000 12263596 52.1% 2007 

7 Anguilla 6049 13677 44.2% 2007 

8 Antigua and Barbuda 30000 69481 43.2% 2007 

9 Argentina 15350000 40301927 38.1% 2007 

10 Armenia 1200000 2971650 40.4% 2007 

11 Aruba 41500 100018 41.5% 2007 

12 Australia 10660000 20434176 52.2% 2007 

13 Austria 3880000 8199783 47.3% 2007 

14 Azerbaijan 5191000 8120247 63.9% 2007 

15 Bahamas 176300 305655 57.7% 2007 

16 Bahrain 352000 708573 49.7% 2007 

17 Bangladesh 68000000 150448339 45.2% 2007 

18 Barbados 128500 280946 45.7% 2007 

19 Belarus 4300000 9724723 44.2% 2007 

20 Belgium 4890000 10392226 47.1% 2007 

21 Belize 113000 294385 38.4% 2007 

22 Benin 3211000 8078314 39.7% 2007 

23 Bermuda 38360 66163 58.0% 2007 

24 Bolivia 4300000 9119152 47.2% 2007 

25 Bosnia and Herzegovina 1026000 4552198 22.5% 2007 

26 Botswana 288400 1815508 15.9% 2007 

27 Brazil 96340000 190010647 50.7% 2007 

28 Brunei Darussalam 180400 374577 48.2% 2007 

29 Bulgaria 3510000 7322858 47.9% 2007 

30 Burkina Faso 5000000 14326203 34.9% 2007 

31 Burundi 2990000 8390505 35.6% 2007 

32 Cambodia 7000000 13995904 50.0% 2007 

33 Cameroon 6394000 18060382 35.4% 2007 

34 Canada 17590000 33390141 52.7% 2007 

35 Cape Verde 120600 423613 28.5% 2007 

36 Cayman Islands 23450 46600 50.3% 2007 

37 Chad 2719000 9885661 27.5% 2007 

38 Chile 6940000 16284741 42.6% 2007 

39 China 798000000 1321851888 60.4% 2007 

47
40 Colombia 20810000 44379598 46.9% 2007 

41 Comoros 144500 711417 20.3% 2007 

42 Congo 15000000 65751512 22.8% 2007 

43 Cook Islands 6820 21750 31.4% 2007 

44 Costa Rica 1866000 4133884 45.1% 2007 

45 Côte d'ivoire 6738000 18013409 37.4% 2007 

46 Croatia 1720000 4493312 38.3% 2007 

47 Cuba 4820000 11394043 42.3% 2007 

48 Cyprus 371000 773000 48.0% 2006 

49 Czech Republic 5310000 10228744 51.9% 2007 

50 Denmark 2910000 5468120 53.2% 2007 

51 Djibouti 282000 496374 56.8% 2007 

52 Dominica 25000 72386 34.5% 2007 

53 Dominican Republic 3896000 9365818 41.6% 2007 

54 Ecuador 4570000 13755680 33.2% 2007 

55 Egypt 21800000 80335036 27.1% 2007 

56 El Salvador 2856000 6948073 41.1% 2007 

57 Estonia 673000 1315912 51.1% 2007 

58 Ethiopia 27270000 76511887 35.6% 2007 

59 Falkland Islands (Malvinas) 1724 3105 55.5% 2007 

60 Faroe Islands 24250 47511 51.0% 2007 

61 Federated States of Micronesia 37410 107862 34.7% 2007 

62 Fiji 137000 918675 14.9% 2007 

63 Finland 2620000 5238460 50.0% 2007 

64 France 27880000 63713926 43.8% 2007 

65 French Polynesia 65930 278963 23.6% 2007 

66 Gabon 581000 1454867 39.9% 2007 

67 Gambia 400000 1688359 23.7% 2007 

68 Gaza Strip 259000 1482405 17.5% 2007 

69 Georgia 2040000 4646003 43.9% 2007 

70 Germany 43660000 82400996 53.0% 2007 

71 Ghana 10870000 22931299 47.4% 2007 

72 Gibraltar 12690 27967 45.4% 2007 

73 Greece 4880000 10706290 45.6% 2007 

74 Greenland 32120 56344 57.0% 2007 

75 Grenada 42300 89971 47.0% 2007 

76 Guam 62050 173456 35.8% 2007 

77 Guatemala 5020000 12728111 39.4% 2007 

78 Guernsey 31470 65573 48.0% 2007 

79 Guinea 3700000 9947814 37.2% 2007 

80 Guinea-Bissau 480000 1472780 32.6% 2007 

81 Guyana 418000 769095 54.3% 2007 

82 Haiti 3600000 8706497 41.3% 2007 

83 Honduras 2589000 7483763 34.6% 2007 

84 Hong Kong 3630000 6980412 52.0% 2007 

85 Hungary 4200000 9956108 42.2% 2007 

86 Iceland 173000 301931 57.3% 2007 

48
87 India 509300000 1129866154 45.1% 2007 

88 Indonesia 108200000 234693997 46.1% 2007 

89 Iran, Islamic Republic of 24360000 65397521 37.2% 2007 

90 Iraq 7400000 27499638 26.9% 2007 

91 Ireland 2120000 4109086 51.6% 2007 

92 Isle of Man 39690 75831 52.3% 2007 

93 Israel 2600000 6426679 40.5% 2007 

94 Italy 24630000 58147733 42.4% 2007 

95 Jamaica 1100000 2780132 39.6% 2007 

96 Japan 66440000 127433494 52.1% 2007 

97 Jersey 53560 91321 58.7% 2007 

98 Kazakhstan 7834000 15284929 51.3% 2007 

99 Kenya 1955000 36913721 5.3% 2007 

100 Kiribati 7870 107817 7.3% 2007 

101 Korea, Democratic People's Republic of 9600000 23301725 41.2% 2007 

102 Korea, Republic of 23770000 49044790 48.5% 2007 

103 Kuwait 1136000 2505559 45.3% 2007 

104 Kyrgyzstan 2700000 5284149 51.1% 2007 

105 Lao People's Democratic Republic 2100000 6521998 32.2% 2007 

106 Latvia 1136000 2259810 50.3% 2007 

107 Lebanon 1500000 3925502 38.2% 2007 

108 Lesotho 838000 2125262 39.4% 2007 

109 Libyan Arab Jamahiriya 1787000 6036914 29.6% 2007 

110 Liechtenstein 29500 34247 86.1% 2007 

111 Lithuania 1617000 3575439 45.2% 2007 

112 Luxembourg 203000 480222 42.3% 2007 

113 Macau 248000 456989 54.3% 2007 

114 

Macedonia, the former Yugoslav 

Republic of 899000 2055915 43.7% 2007 

115 Madagascar 7300000 19448815 37.5% 2007 

116 Malawi 4500000 13603181 33.1% 2007 

117 Malaysia 10730000 24821286 43.2% 2007 

118 Maldives 101300 369031 27.5% 2007 

119 Mali 3930000 11995402 32.8% 2007 

120 Malta 164000 401880 40.8% 2007 

121 Marshall Islands 14680 61815 23.7% 2007 

122 Mauritania 786000 3270065 24.0% 2007 

123 Mauritius 555000 1250882 44.4% 2007 

124 Mayotte 44560 208783 21.3% 2007 

125 Mexico 38090000 108700891 35.0% 2007 

126 Moldova, Republic of 1339000 4320490 31.0% 2007 

127 Mongolia 1577000 2951786 53.4% 2007 

128 Montenegro 144000 623000 23.1% 2005 

129 Morocco 11250000 33757175 33.3% 2007 

130 Mozambique 9400000 20905585 45.0% 2007 

131 Myanmar 28490000 47373958 60.1% 2007 

132 Namibia 653000 2055080 31.8% 2007 

49
133 Nepal 11110000 28901790 38.4% 2007 

134 Netherlands 7600000 16570613 45.9% 2007 

135 Netherlands Antilles 83600 223652 37.4% 2007 

136 New Caledonia 78990 221943 35.6% 2007 

137 New Zealand 2180000 4115771 53.0% 2007 

138 Nicaragua 2261000 5675356 39.8% 2007 

139 Niger 70000 12894865 0.5% 2007 

140 Nigeria 48990000 135031164 36.3% 2007 

141 Northern Mariana Islands 44470 84546 52.6% 2007 

142 Norway 2420000 4627926 52.3% 2007 

143 Oman 920000 3204897 28.7% 2007 

144 Pakistan 48290000 164741924 29.3% 2007 

145 Palau 9777 20842 46.9% 2007 

146 Panama 1441000 3242173 44.4% 2007 

147 Papua New Guinea 3477000 5795887 60.0% 2007 

148 Paraguay 2742000 6669086 41.1% 2007 

149 Peru 9210000 28674757 32.1% 2007 

150 Philippines 35790000 91077287 39.3% 2007 

151 Pitcairn 15 48 31.3% 2007 

152 Poland 17260000 38518241 44.8% 2007 

153 Portugal 5580000 10642836 52.4% 2007 

154 Puerto Rico 1300000 3944259 33.0% 2007 

155 Qatar 508000 907229 56.0% 2007 

156 Romania 9330000 22276056 41.9% 2007 

157 Russian Federation 73880000 141377752 52.3% 2007 

158 Rwanda 4600000 9907509 46.4% 2007 

159 Saint Helena 2486 7543 33.0% 2007 

160 Saint Kitts and Nevis 18170 39349 46.2% 2007 

161 Saint Lucia 43800 170649 25.7% 2007 

162 Saint Pierre and Miquelon 3450 7036 49.0% 2007 

163 Saint Vincent and the Grenadines 41680 118149 35.3% 2007 

164 Samoa 90000 214265 42.0% 2007 

165 San Marino 20470 29615 69.1% 2007 

166 Sao Tome and Principe 35050 199579 17.6% 2007 

167 Saudi Arabia 7125000 27601038 25.8% 2007 

168 Senegal 4749000 12521851 37.9% 2007 

169 Serbia 2069000 7441000 27.8% 2005 

170 Seychelles 30900 81895 37.7% 2007 

171 Sierra Leone 1369000 6144562 22.3% 2007 

172 Singapore 2400000 4553009 52.7% 2007 

173 Slovakia 2629000 5447502 48.3% 2007 

174 Slovenia 1026000 2009245 51.1% 2007 

175 Solomon Islands 249200 566842 44.0% 2007 

176 Somalia 3700000 9118773 40.6% 2007 

177 South Africa 16090000 43997828 36.6% 2007 

178 Spain 21770000 40448191 53.8% 2007 

179 Sri Lanka 7500000 20926315 35.8% 2007 

50
180 Sudan 7415000 39379358 18.8% 2007 

181 Suriname 156700 470784 33.3% 2007 

182 Swaziland 300000 1133066 26.5% 2007 

183 Sweden 4590000 9031088 50.8% 2007 

184 Switzerland 3810000 7554661 50.4% 2007 

185 Syrian Arab Republic 5505000 19314747 28.5% 2007 

186 Taiwan, Province of China 10460000 22858872 45.8% 2007 

187 Tajikistan 3700000 7076598 52.3% 2007 

188 Tanzania, United Republic of 19350000 39384223 49.1% 2007 

189 Thailand 36410000 65068149 56.0% 2007 

190 Togo 1302000 5701579 22.8% 2007 

191 Tonga 33910 116921 29.0% 2007 

192 Trinidad and Tobago 618000 1056608 58.5% 2007 

193 Tunisia 3502000 10276158 34.1% 2007 

194 Turkey 24800000 71158647 34.9% 2007 

195 Turkmenistan 2320000 5097028 45.5% 2007 

196 Turks and Caicos Islands 4848 21746 22.3% 2007 

197 Uganda 13760000 30262610 45.5% 2007 

198 Ukraine 22300000 46299862 48.2% 2007 

199 United Arab Emirates 2968000 4444011 66.8% 2007 

200 United Kingdom 31100000 60776238 51.2% 2007 

201 United States 151400000 301139947 50.3% 2007 

202 Uruguay 1270000 3460607 36.7% 2007 

203 Uzbekistan 14440000 27780059 52.0% 2007 

204 Vanuatu 76410 211971 36.0% 2007 

205 Venezuela 12500000 26023528 48.0% 2007 

206 Viet Nam 44580000 85262356 52.3% 2007 

207 Virgin Islands, British 12770 23552 54.2% 2007 

208 Virgin Islands, U.S. 43980 108448 40.6% 2007 

209 Wallis and Futuna 3104 16309 19.0% 2007 

210 West Bank 568000 2535927 22.4% 2007 

211 Western Sahara 12000 382617 3.1% 2007 

212 Yemen 5759000 22230531 25.9% 2007 

213 Zambia 4903000 11477447 42.7% 2007 

214 Zimbabwe 3958000 12311143 32.1% 2007 

215 

California (treated as a separate region 

than the rest of United States) 17688900 36457549 48.5% 2006 

51
Appendix IV 

Attribute mapping developed for different databases. 

United Nations Compilation, 1993

We performed mapping of the construction material nomenclature used in the UN (1993) 

database to equivalent PAGER structure types shown in below. For example, housing units with 

brick and mud as a construction material of external walls have been mapped with “Unreinforced 

fire brick masonry with mud mortar” or UFB1 structure type. Similarly, housing units made of the 

Stone/Concrete category have been assumed to be made of load-bearing walls of stone masonry 

and concrete roof and are mapped to “Field Stone masonry with cement mortar and reinforced 

concrete bond beam” which supports the concrete or other roof sheeting. Though for most common 

types and in case of large number of countries, the mapping seemed to be acceptable and simple, 

for typical cases such as reinforced concrete buildings with infill brick or concrete block masonry 

construction, it was likely that the existing database classifies them as belonging to either brick or 

to concrete block construction, whereas, in reality, the structural system is made of reinforced 

concrete beams-column elements. Under these circumstances, we used expert opinion supported 

with Internet research for understanding the most common construction types in that country to 

assign them to the appropriate structure type. Future research and (or) data compiled from rigorous 

field investigation by professional engineers could help better assess the distribution of such 

housing units and to reclassify them. 

According the database, 31 percent of occupied housing units in case of the Dominican 

Republic have been constructed using ”concrete” as an external wall material. Since no specific 

information about its structural system is available in the original data, it was extremely difficult to 

distribute this stock in several concrete structure type categories based on limited country-specific 

research; hence we have categorized this building stock as general concrete (C) structure type. The 

objective was to involve minimum interference with basic information about fraction available 

from a raw data source. It is worth noting that other databases, such as WHE-PAGER expert 

opinion survey, recent housing census or published literature will supersede this particular database 

(which is old and has “Low” quality assigned) in later phases of inventory development. However, 

in the absence of any other information for this country, these data will remain part of PAGER 

inventory with lowest confidence until more recent updating can be made based on further 

research. 

In general, it has been found that reinforced concrete moment frame with infill masonry 

construction is common in rapidly developing countries and are limited to urban areas and forms 

very small fraction of total housing types in a country. It is important to note that the existing UN 

1993 database has certain limitations and is classified with “Low” in the PAGER rating category 

database as described in the previous section. Hence the processed database should be used within 

its limitations. 

52
Sr. No. UN (1993) Descriptions 

(Construction of Wall) 

PAGER 

Structure 

Type 

1 Adobe; Adobe and mud; Adobe and wood A 

2 Concrete; Concrete/Bricks C 

3 Cement C3 

4 Iron cement skeleton C5 

5 Stone/Cement DS3 

6 Fibro-asbestos; Local material; Makeshift or improvised material; Metal sheets; 

Metal/Asbestos; Poles; Poles/mud; Pre-fabricated; Quirintin; Quirintin/mud; Reused material; Traditional bure material; Zinc 

INF 

7 Clay; Cob; Mud; Mud and Concrete; Mud brick; Mud brick/blocks M 

8 Wood and Mud M2 

9 Stone; Wood and Stone RS 

10 Rock Sheet RS1 

11 Stone/Clay RS2 

12 Stone/Concrete RS5 

13 Blockets and Cement; Blocks; Blocks and bricks; Brick/stone; Cement block; 

Cement/brick; Concrete blocks; Stone/block 

UCB 

14 Baked/burned brick; Brick; Brick and wood; Brick, stone; Brick, Wooden beam; 

Masonry 

UFB 

15 Stone and mud; Stone/mud UFB1 

16 Brick and cement; Brick, Iron beam; Brick/concrete; Stone and cement UFB3 

17 All wood; Bamboo; Bamboo and thatch; Bamboo, palm; Bush; Cane; 

Cane/woodsticks; Cardboard plates; Cement/wood with iron roof; Palm; Palm 

planks; Palm/lattice; Panel; Permanent tin or Corrugated iron; Planks; Seasoned 

timber; Solid wood; Straw/Bamboo; Stucco; Thatch; Timber; Unseasoned timber; 

Waste Material; Wattle and Daub walls; Weaved straw; Wood; Wood and 

Bamboo; Wood and thatch; Wood wall and thatch roof; Wood, straw; Wood/Zinc 

W 

18 Wood and cement; Wood/Brick; Wood/Concrete; Wood/Iron W1 

19 Not stated; Other; UNK 

UN-HABITAT Compilation, 2007 

Unlike the UN 1993 database, which has very limited description of construction material 

of external walls, UN 2007 database had quite a number of descriptions and it was quite time 

consuming to do the mapping of these descriptions with standard PAGER construction types. Table 

2 shows typical raw data on housing units by construction material for Tanzania in the year 2004. It 

is interesting to note that 51 percent of housing units in this country have external walls made of 

cement bricks, which can be similar to unreinforced concrete block masonry construction or 

PAGER (UCB) type. 

We have developed a new mapping scheme similar to the mapping scheme of the UN 1993 

database using descriptions provided in the UN 2007 database. We used engineering judgment 

along with general Internet research and other published articles for specific cases to assign 

appropriate PAGER structure type against each wall material description as shown in below. For 

example Quincha type dwellings, common in Peru (http://en.wikipedia.org/wiki/Quincha), consist 

of light wooden frames infilled with interwoven cane and coated with a mixture of mud and plaster 

53
on both sides (Blondet, Torrealva and others, 2002). Such construction results in a very light and 

flexible structure and is remarkably good for heat insulation. In Peru, ancestral building methods 

such as adobe, tapial, and quincha take advantage of the local resources for housing construction. 

These construction methods have been used for centuries. Two-story houses were usually built with 

adobe first story and a lighter second story made out of Quincha. We classify such construction 

type into PAGER M2 category, as the primary construction material is wood and mud and the 

overall assembly of light wooden frame with mud is not designed to resist earthquake ground 

shaking. Another example includes sillar which is a consolidated volcanic ash generally used in the 

form of sillar blocks in traditional masonry constructions. We classified them into PAGER DS3 

category. 

Sr. 

No. 

UN-HABITAT (2007) Descriptions 

(Construction of Wall) 

PAGER 

Structure 

Type 

1 Adobe; Adobe (mud bricks); Adobe-o-tapia; Adobe or "taquezal"; Adobe with 

sod; Brique en terre non cuite; Covered adobe; Earthen Bricks; Sundried bricks; 

Unbaked bricks; Unburnt bricks; Uncovered adobe 

A 

2 Cement C3 

3 Brique cuite; Stone blocks DS 

4 Piedra o sillar con cal o cemento DS3 

5 3-Banco; 4-Semi-dur; Blank Field (No description); Carton; Disposable 

material; Grass; Half solid; Hard; No walls; Other finished; Plastic/Cardboard; 

Plastic/Carton/Used metal sheet; Prefab; Prefab material; 

Semihard/semifinished/other; Tin; Tole; Tripley; Waste material; Without walls; 

Zinc, canvas, plastics 

INF 

6 Cob/daub/mud/clay; Compact dirt/mud; Dung; Earth; Earth, sand; Mud; Mud 

and Cement; Mud stones; Mud unpolished; Mud walls; Other dung materials; 

Tabique / Chinche 

M 

7 Terre battue M1 

8 Mud and pole; Poles and mud; Quincha (cañas con barro) M2 

9 Natural stone; Plaster/Finished; Rocks; Stone RS 

10 Piedra con barro; Stone with mud RS2 

11 Stone with lime / cement; Stone with lime / cement RS3 

12 Stone with cement RS4 

13 Block of cement; Blocks / bricks; Breeze blocks/parpens/stones/cement; Cement 

/ Concrete blocks; Cement blocks; Cement bricks; Ciment/aglo/parpaing; Cinder 

blocks; Ladrillo o bloque de cemento 

UCB 

14 Alvenaria (finished); Baked bricks; Bare Brick/Cement Block; Bricks; 

Bricks/polished wood/premanufactured material; Finished bricks; Mixed brick 

UFB 

15 Burnt bricks with mud UFB1 

16 Baked bricks, cement; Brick/cement; Burnt bricks with cement UFB3 

17 Bamboo; Bamboo, planks; Bamboo, straw, other plants; 

Bark/straw/branch/bamboo; Bois/planches; Cane / trunks / bamboo / reed; Cane, 

palmtree, trunks; Corrugated iron 2-Toles; Corrugated iron sheet; Corrugated 

Iron/Zinc; Estera; Madera; Metalic sheet; Palm branches; Palm leaves; Palm 

tree; Palm tree "Yagua"; Palm, straw; Planks; Plywood; Polished wood; Raw 

wood; Straw, cane or palm; Straw/palm/bamboo/wood; Thatched; Timber; 

Wood; Wood /bamboo; Wood and grass; Wood planks; Wood planks / shingles; 

Wood poles; Wood/branches; 

W 

18 Ceramic tiles; Plycem or Nicalit tiles; Shingle "Tejamanil"; Wood / Concrete W1 

19 Bamboo / wood; Bamboo with mud plaster; Jute/Bamboo/mud (katcha) W2 

20 Others, Not specified, Unknown UNK 

54
Housing Census 

The following table provides the classification of terms used in the description of housing units by 

construction material into PAGER structure type. The classification is similar to the procedure 

adopted for compilation of data from the UN database as explained in the previous section. 

Sr. 

No. 

Description (Construction of Wall) PAGER 

Structure 

Type 

1 Mud, Unburnt Brick; Un-baked bricks/Earth Bound; Adobe A 

2 Reinforced Concrete Construction (RCC); Concrete; Concrete/ brick/ stone C 

3 Fibro/asbestos cement; Others; Grass, thatch, Bamboo; Plastic, Polythene; 

Galvanized Iron (GI), Metal, Asbestos sheet; Galvanized iron/ aluminum; 

Asbestos; Glass; Makeshift/ salvaged/ improvised materials; Others/not reported; 

No walls; Mainly permanent materials; Non-permanent materials; Other 

Materials; Informal; Reused materials; 

INF 

4 Stone RS 

5 Steel-framed S 

6 Baked Bricks/Blocks/Stone; Block, Brick or Stone; Block UCB 

7 Double brick; Burnt brick; Brick UFB 

8 Mud Bonded Bricks/Stone UFB1 

9 Cement Bonded Bricks/Stones and Concrete; Cement or brick UFB3 

10 Wooden; Wood; Timber; Wood/Bamboo; Bamboo/ sawali/ cogon/ nipa; 

Wood/Branches; Wood and cement or brick; Wood 

W 

11 Brick veneer W1 

12 Wooden and fire-proofed; Half concrete/ brick/ stone and half wood W2 

13 Unknown; Other UNK 

World Housing Encyclopedia 

The performance of building types during adverse ground shaking is characterized by its 

capacity to resist lateral forces caused immediately after an earthquake. Structural engineers 

responsible for designing earthquake resistant buildings, try to provide sufficient capacity within 

the structural system through arrangements of beam-columns, shear walls, reinforced masonry 

walls etc., to create adequate ductile lateral load resisting system. In most of the non-engineered 

buildings, the load-bearing walls (masonry walls made from brick, stone, or other material) or 

wood frames provide lateral load resistant system. Ideally, each construction type should be 

identified by its lateral load resisting system; however, such data are expensive to gather and it also 

requires involvement of engineering professionals. At the global level, it is extremely difficult to 

survey each individual building and compile data of fraction of various construction types within a 

certain geographic region to develop a global building inventory database. At regional levels, such 

exercises have been carried out based on engineering characterization of construction types; 

however, it has proved extremely time consuming and expensive even for very small geographic 

regions (portion of metropolitan area, city or suburb) (Faccioli, Pessina and others, 1999; Bommer, 

Spence and others, 2002; Erdik and Fahjan, 2006). Most of the housing surveys conducted around 

55
56

the world compile data based on predominant material used in the construction of building 

components such as wall, roof or floor. The mapping scheme on wall construction material to 

identify construction types has been used and the WHE housing types have been assigned with 

PAGER structure type, as shown in Appendix V. 
Appendix V 

Compilation of the Earthquake Engineering Research Institute’s (EERI, 2007) World Housing Encycdatabase. 

Occupan

PAGER cy 

Name of Structure No. of No. of (family Work Night Rural Country Name of Housing Type Category Stories Units per unit) hours hours /Urban 

Germany 

Prefabricated metal construction of the Modern Movement S5L 2-4 24 

3-5 person per unit Other Other Suburban 

Greece 

Multistory reinforced concrete frame building C2M 4-6 16 1 5 - 10 > 20 Urban 

Greece 

Load-bearing stone masonry building RS4 2-4 1 1-2 < 5 5 - 10 Both 

Greece 

Reinforced concrete frame building with an independent vertical extension C3L 2-4 2-3 2-3 < 5 5 - 10 Both 

Italy Single-family stone masonry house DS1 2-4 2 1-2 < 5 5 - 10 Urban 

Italy 

Single-family historic brick masonry house (Casa unifamiliare in centro storico, Centro Italia) UFB3 2-4 1 1 < 5 < 5 Urban 

Italy Reinforced concrete frame building C3M 4-10 10-30 1 > 20 > 20 Urban Italy 

Brick masonry farmhouse with a "dead door". UFB3 2-4 1 1 5 - 10 5 - 10 Rural "Casa Torre" construction: 

Italy 

multistory tower masonry with stone pillars and wood or arched beams MS 4-7 1 1 5 - 10 5 - 10 Urban 

Italy 

Unreinforced stone wall housing (upper income) RS2 4-6 1 1 < 5 < 5 Both 

Italy 

Unreinforced stone wall rural housing (lower and middle income) RS2 4 1-2 1-2 < 5 5 - 10 Rural 

Portugal 

Historic, braced frame timber buildings with masonry infill ('Pombalino' buildings) W2 4-5 6-8 1 > 20 

> 20 (5-10 for nonresidential) Urban 

Reinforced concrete frame 

Romania 

structure with diagonal bracings and brick infill walls C1M 5-8 20 1 Other Other Urban 

Romania 

Reinforced concrete cast-in situ shear wall buildings ("OD"-type, C2H 10-11 20-100 1 Other Other Urban 

57
with "fagure" plan) 

Romania 

Precast concrete panel apartment buildings PC1 4-11 150 1 > 20 > 20 Urban 

Romania 

A single-family, two-story house with brick walls and timber floors UFB2 2-4 1 1 < 5 < 5 Both 

Romania 

One family one-story house, also called "wagon house" UFB3 1 1 1 < 5 < 5 Urban 

Romania 

Block of flats with 11 floors out of cast-in-situ concrete, gliding frameworks C2H 11-20 44 1 > 20 > 20 Urban 

Romania 

Early reinforced concrete frame condominium building with masonry infill walls designed for gravity loads only C3M 5-10 

25 (variable) 1 Other Other Urban 

Romania 

Medium/high rise moment resisting reinforced concrete frame buildings. C3H 4-18 30 1 > 20 > 20 Urban 

Russian Federation 

Small concrete block masonry walls with concrete floors and roofs UCB 2-4 12 - 36 1 10 - 20 >20 Both 

Russian Federation 

Large concrete block walls with reinforced concrete floors and roof (typical series: 1-306c, 1-307c, 114c) UCB 2-4 

12 - 64 (48) 1 >20 Other Both 

Russian Federation 

Large reinforced concrete panel buildings ( Series 122, 135 and 1-464c) PC 4-9 80 1 >20 Other Urban 

Russian Federation Timber log building W4 1 2-4 1 5 - 10 10 - 20 Rural Russian Federation 

Wood panel wall buildings (typical seria 181-115-77 cm of "Giprolesprom") W1 1 2 1 < 5 5 - 10 Rural 

Russian Federation 

Buildings protected with "disengaging reserve elements" C1M 5-10 

60 (32-64 ) 1 > 20 > 20 Urban 

Serbia and Montenegro 

Precast, prestressed concrete frame structure with concrete shear walls PC2M 5-10 

40-200 (100) 1 > 20 > 20 Urban 

Serbia and Confined masonry building with RM2L 2-4 20 (1 - 50 1 > 20 (5-20) > 20 Both 

58
Montenegro concrete floor slabs ) 

Slovenia Rubble-stone masonry house RS3 2-3 1 or 2 1 < 5 < 5 Both Slovenia 

Unreinforced brick masonry apartment building UFB3 4-6 

12-60 (4-10 per floor) 1 > 20 > 20 Urban 

Slovenia Confined brick masonry house RM2L 1-2 1 - 2 1 or 2 < 5 5 - 10 Both Switzerland 

Fachwerkhaus in Dreiländereck (Half-timbered house in the "border triangle") W2 1 1 1 or 2 < 5 5 - 10 Both 

Switzerland 

Urban residential buildings of the 19th century in the city of Basel 

UFB3 or MS 4-5 4 - 5 1 5 - 10 10 - 20 Urban 

Turkey 

Reinforced concrete frame building with masonry infills C3M 3-7 12 1 10 - 20 > 20 Urban 

Turkey Tunnel form building C2H 10-15 40-50 1 > 20 > 20 

Urban and Suburban 

Algeria 

Single-family reinforced concrete frame houses C3L 1-3 1 1 < 5 5 - 10 Suburban 

Algeria Stone masonry apartment building MS 5-10 10-15 1 10 - 20 > 20 Urban Malawi 

Rammed earth house with pitched roof (Nyumba yo dinda OR Nyumba ya mdindo) RE 1 1 1 < 5 5 - 10 Rural 

Malawi 

Rural mud wall building (nyumba yo mata OR ndiwula) A2 1 1 1 < 5 5 - 10 Rural 

Malawi 

Unburnt brick wall building with pitched roof (nyumba ya zidina) UFB1 1 1 1 < 5 5 - 10 Both 

Canada Single-family wood frame house W1 1 1 1 < 5 5 - 10 Both Canada 

Concrete shear wall highrise buildings C2H 12-35 100-200 1 300 500 Urban 

Mexico 

Reinforced concrete multistory buildings C1H 10-25 30 1 10 > 50 Urban 

United States of America Wood frame single family house W1 1 1 1 or 2 < 5 5 - 10 Both United States of America 

Reinforced Concrete Moment Frame Building without Seismic Details C3 4-15 20-70 1 > 20 > 20 Urban 

El Salvador Vivienda de Adobe (Adobe house) A2 1 1 1 < 5 5 - 10 Both 

59
Argentina Confined block masonry house UCB 1-2 1 1 < 5 < 5 Urban 

Argentina 

Solid brick masonry house with composite hollow clay tile and concrete joist roof slabs UFB4 1 1 1 5 - 10 5 - 10 Urban 

Argentina 

Traditional adobe house with reinforcement A4 1 1 1 5 - 10 5 - 10 Rural 

Argentina 

Traditional adobe house without seismic features A1 1 1 1 5 - 10 5 - 10 Rural 

Chile 

Buildings with hybrid masonry walls UCB 2-4 8 1 or 2 5 - 10 > 20 Urban 

Chile 

Concrete frame and shear wall building C2H 10-30 

NonResidential Not applicable > 20 5 - 10 Urban 

Chile Concrete shear wall buildings C2 4-30 

70 (4 - 10 per floor) 1 > 20 > 20 Urban 

Chile Confined block masonry building UCB 2-4 8 1 or 2 5 - 10 > 20 Both 

Chile 

Reinforced clay/concrete block masonry building RM2L 1-3 8 1 or 2 10 - 20 > 20 Both 

Chile 

Steel frame buildings with shear walls S4 3-24 80 1 > 20 > 20 Urban 

Colombia 

Clay brick/concrete block masonry walls with concrete floors (predating seismic codes or with a few seismic features) UCB 5 5 1 5 - 10 > 20 Urban 

Colombia Concrete Shear Wall Buildings C2H 7-20 40 1 > 20 Other Urban Colombia 

Gravity concrete frame buildings (predating seismic codes) C3M 5 5 1 5 - 10 > 20 Urban 

Colombia 

Non-engineered unreinforced brick masonry building 

UFB3 or UFB4 2-4 3 1 < 5 5 - 10 Both 

Peru Adobe house A1 1 1 1 or 2 < 5 5 - 10 Both 

Peru Confined masonry building RM2M 4-6 6 1 or 2 10 - 20 > 20 Urban 

Peru Confined masonry house RM2L 2-3 1 1 < 5 5 - 10 Both 

Venezuela 

Popular, non-engineered urban housing on flat terrain C3L 2-3 2 1 or 2 < 5 10 - 20 Urban 

Bangladesh 

Single-story brick masonry house (EMSB1) UFB3 1 1 1 < 5 5 - 10 Both 

China 

Multistory base-isolated brick masonry building with reinforced concrete floors and roof 

C1H (Base Isolated) 6 (5-10) 32 1 > 20 Other Urban 

Cyprus 

Gravity designed reinforced concrete frame buildings with unreinforced masonry infill walls. C3M 3-5 8 - 14 1 5 - 10 > 20 Both 

60
India 

Rubble stone masonry walls with timber frame and timber roof RS3 1 1 1 or 2 5 - 10 10 - 20 Rural 

India 

Reinforced concrete frame building with masonry infill walls designed for gravity loads C3M 5-10 

10 ) 

(8-20 1 

(extended) 10 - 20 > 20 Both 

India 

Unreinforced brick masonry walls in mud mortar with flat timber roof UFB2 1 1 

1 (extended) 5 - 10 10 - 20 Both 

India 

Unreinforced brick masonry building with reinforced concrete roof slab UFB4 1-4 1 1 or 2 5 - 10 10 - 20 Both 

India 

Unreinforced brick masonry walls with pitched clay tile roof UFB3 1 1 1 or 2 < 5 > 20 Both 

India Rural mud house with pitched roof M2 1 1 OR 2 1 < 5 5 - 10 Rural India 

Traditional rural house in Kutch region of India (bhonga) A2 1 1 1 < 5 5 - 10 Rural 

India 

Low-strength dressed stone masonry buildings MS 1-4 

8 ) 

(16 - 20 

1 5 - 10 > 20 Both 

India 

Timber Frame Brick House with Attic UFB2 1 2 1 < 5 5 - 10 Both 

Indonesia 

Unreinforced clay brick masonry house UFB3 1 1 1 < 5 5 - 10 Rural 

Iran 

Steel frame with semi-rigid "Khorjini" connections and jack arch roof "Taagh-e-Zarbi". S5M 2-5 2-6 1 5 - 10 10 - 20 Both 

Iran 

Semi-rigid steel frame with "Khorjinee" connections S2M 4-6 8 1 5 - 10 10 - 20 Both 

Iran 

Confined brick masonry building with concrete tie columns and beams. UFB3 4-6 1 1 < 5 5 - 10 Rural 

Iran Adobe House A1 1 1 1 < 5 < 5 Both Iran 

Stonework Building with Wooden Joist Roof RS2 1 1 1 < 5 5 - 10 Rural 

Iran 

Four arches (Char taaqi) with dome-roof structures, and unreinforced brick and adobe materials. 

MS or UFB1 1 1 OR 2 

Not applicable > 20 5 - 10 Both 

Iran 

Earring system (Shekanj) in domeroof structures with unreinforced brick and adobe materials. UFB1 

Not Available 1 1 < 5 5 - 10 Both 

Japan Single-family wooden house W1 1 1 1 < 5 < 5 Both 

Precast reinforced concrete frame 

60 (36-120 

Kyrgyzstan 

building with cruciform and linearbeam elements (Series 106) PC2H 9-12 families per building) 1 or 2 > 20 > 20 Urban 

Kyrgyzstan 

Traditional wood frame construction (Yurta) W 1 1 1 5 - 10 5 - 10 Rural 

61
Kyrgyzstan Single-family brick masonry house UFB4 2 1 1 < 5 5 - 10 Both 

Kyrgyzstan 

Prefabricated concrete panel buildings with monolithic panel joints (seria 105) PC1 5-9 

60 80) 

(40-

1 > 20 > 20 Urban 

Kyrgyzstan 

Reinforced concrete frame buildings without beams (seria KUB) PC2M 5-12 

36 (10-120) 1 > 20 > 20 Urban 

Kyrgyzstan 

Buildings with cast-in-situ loadbearing reinforced concrete walls C2H 4-18 

54 (20-90 families per building) 1 > 20 > 20 Urban 

Kyrgyzstan 

Two-story unreinforced brick masonry building with wooden floors UFB2 2 8 - 16 1 10 - 20 > 20 Urban 

Kyrgyzstan 

Houses with mud walls and thatch roofs M2 1 1 1 < 5 5 - 10 Both 

Kyrgyzstan 

Buildings with hollow clay tile loadbearing walls and precast concrete floor slabs UFB4 4 

64 (1 or 2 per floor) 1 > 20 > 20 Urban 

Kyrgyzstan 

Load-bearing wall buildings protected with the "sliding belt" base isolation system 

UFB4 with base isolation or PC with base isolation 9 (5-10) 

64 (1 or 2 per floor) 1 > 20 > 20 Urban 

Malaysia 

Reinforced concrete frame building with timber roof 

C3L (with timber roof) 2 (2-4) 10 1 < 5 5 - 10 Both 

Nepal 

Traditional oval-shaped rural stone house RS2 2 (2-4) 1 1 < 5 5 - 10 Rural 

Nepal 

Uncoursed rubble stone masonry walls with timber floor and roof MS 2 (2-4) 1 1 or 2 5 - 10 5 - 10 Both 

Nepal 

Traditional Nawari house in Kathmandu Valley UFB2 3-5 1 1 or 2 5 - 10 10 - 20 Urban 

Pakistan 

Unreinforced brick masonry residential building UFB4 1-3 1 1 or 2 < 5 5 - 10 Urban 

Pakistan Stone Masonry RS2 

Not Available 1 1 or 2 5 - 10 10 - 20 Rural 

62
Reinforced concrete frame with 

Palestinian Territories 

infill walls designed for gravity loading C3M 4-5 10 (8-14) 1 5 - 10 > 20 Both 

Unreinforced concrete and 

Palestinian Territories 

masonry bearing wall construction (designed for gravity loads only) MS 2 (2-4) 4 1 5 - 10 10 - 20 Both 

Syrian Arab Republic 

Reinforced concrete frame with concrete shear walls - dual system C2H 6-15 

45 ) 

(24-45 

1 > 20 > 20 Urban 

Syrian Arab Republic 

Moment resisting frame designed for gravity loads only C3L 3-5 12 1 10 - 20 > 20 Urban 

Taiwan 

Street front building with arcade at the first floor (pre-1970s construction) C3M 4-5 

10 from 6-10) 

(varies 

1 10 - 20 

Other (more than 50) Both 

Taiwan 

Street front building with arcade at the first floor (contemporary construction) C3M 4-5 6 - 10 1 10 - 20 

Other (more than 50) Both 

Taiwan 

High-rise, reinforced concrete buildings with open space at the ground floor C1H 12-20 20 (10-30) 1 10 - 20 Other Both 

Uzbekistan 

Precast reinforced concrete frame panel system of seria IIS-04 PC2H 9-12 60 1 > 20 Other Urban 

Kazakhstan 

Prefabricated large panel concrete buildings with two interior longitudinal walls PC1 5-9 81 1 > 20 Other Urban 

63
Appendix VI 

Estimated average occupancy (day and night) and average number of units within each model using Appendix V. 

Label Description Occupants Average No. 

of Units 

Sourc

Work 

Hours 

Night 

Hours 

W WOOD 5 to 10 5 to 10 1 EERI (20

W1 Wood Frame, Wood Stud, Wood, Stucco, or Brick 

Veneer 

< 5 5 to 10 1 EERI (20

W2 Wood Frame, Heavy Members, Diagonals or Bamboo 

Lattice, Mud Infill 

> 20 > 20 7 EERI (20

W3 Wood Frame, Prefabricated Steel Stud Panels, Wood 

or Stucco Exterior Walls 

5 to 10 5 to 10 1 By Judgm

W4 Log building 5 to 10 10 to 20 3 EERI (20S STEEL 10 to 20 > 20 6 By Judgm

S1 Steel Moment Frame 10 to 20 > 20 6 By Judgm

S1L Low-Rise 5 to 10 10 to 20 6 By Judgm

S1M Mid-Rise 10 to 20 > 20 8 By Judgm

S1H High-Rise > 20 500 45 By Judgm

S2 Steel Braced Frame > 20 > 20 70 By Judgm

S2L Low-Rise 5 to 10 10 to 20 6 By Judgm

S2M Mid-Rise 10 to 20 > 20 8 EERI (20

S2H High-Rise > 20 500 45 By Judgm

S3 Steel Light Frame > 20 > 20 70 By Judgm

S4 Steel Frame with Cast-in-Place Concrete Shear Walls > 20 > 20 80 EERI (20

S4L Low-Rise 5 to 10 10 to 20 6 By Judgm

S4M Mid-Rise 10 to 20 > 20 8 By Judgm

S4H High-Rise > 20 500 45 By Judgm

S5 Steel Frame with Unreinforced Masonry Infill Walls > 20 > 20 70 By Judgm

64
S5L Low-Rise 5 to 10 10 to 20 6 By Judgm

S5M Mid-Rise 5 to 10 10 to 20 4 EERI (20

S5H High-Rise > 20 500 45 By Judgm

C REINFORCED CONCRETE 10 to 20 > 20 6 By Judgm

C1 Ductile Reinforced Concrete Moment Frame 10 to 20 > 20 6 By JudgmC1L Low-Rise 5 to 10 10 to 20 6 By Judgm

C1M Mid-Rise > 20 > 20 60 EERI (20C1H High-Rise > 20 > 50 30 EERI (20

C2 Reinforced Concrete Shear Walls > 20 > 20 70 EERI (20

C2L Low-Rise 5 to 10 10 to 20 6 By Judgm

C2M Mid-Rise 5 to 10 > 20 16 EERI (20

C2H High-Rise > 20 500 45 EERI (20

C3 Nonductile Reinforced Concrete Frame with Masonry 

Infill Walls 

> 20 > 20 45 EERI (20

C3L Low-Rise < 5 5 to 10 12 EERI (20C3M Mid-Rise 10 to 20 > 20 10 EERI (20C3H High-Rise > 20 > 20 30 EERI (20

C4 Nonductile Reinforced Concrete Frame without 

Masonry Infill Walls 

10 to 20 > 20 6 By Judgm

C4L Low-Rise 5 to 10 10 to 20 6 By Judgm

C4M Mid-Rise 10 to 20 > 20 10 By Judgm

C4H High-Rise > 20 500 45 By JudgmC5 Steel Reinforced Concrete

Reinforced Concrete) 

(Steel Members Encased in 10 to 20 > 20 6 By Judgm

C5L Low-Rise 5 to 10 10 to 20 6 By Judgm

C5M Mid-Rise 10 to 20 > 20 10 By Judgm

C5H High-Rise > 20 500 45 By JudgmPC1 Precast Concrete Tilt-Up Walls > 20 > 20 150 EERI (20

PC2 Precast Concrete Frames with Concrete Shear Walls 10 to 20 > 20 6 By JudgmPC2L Low-Rise 5 to 10 10 to 20 6 By Judgm

PC2M Mid-Rise > 20 > 20 100 EERI (20

PC2H High-Rise > 20 > 20 60 EERI (20

65
RM REINFORCED MASONRY < 5 5 to 10 2 By Judgm

RM1 Reinforced Masonry Bearing Walls with Wood or 

Metal Deck Diaphragms 

< 5 5 to 10 2 By Judgm

RM1L Low-Rise < 5 > 20 20 By JudgmRM1M Mid-Rise (4+ stories) 10 to 20 > 20 6 By JudgmRM2 Reinforced Masonry Bearing Walls with Concrete 

Diaphragms 

10 to 20 > 20 6 By Judgm

RM2L Low-Rise < 5 > 20 20 EERI (20RM2M Mid-Rise 10 to 20 > 20 6 EERI (20RM2H High-Rise > 20 > 20 60 By JudgmMH Mobile Homes < 5 5 to 10 2 By JudgmM MUD WALLS < 5 5 to 10 2 By JudgmM1 Mud Walls without Horizontal Wood Elements < 5 5 to 10 2 By JudgmM2 Mud Walls with Horizontal Wood Elements < 5 5 to 10 2 EERI (20A ADOBE BLOCK (UNBAKED DRIED MUD 

BLOCK) WALLS 

< 5 5 to 10 1 By Judgm

A1 Adobe Block, Mud Mortar, Wood Roof and Floors < 5 5 to 10 1 EERI (20A2 Same as A1, Bamboo, Straw, and Thatch Roof < 5 5 to 10 1 EERI (20A3 Same as A1, Cement-Sand Mortar < 5 5 to 10 1 By JudgmA4 Same as A1, Reinforced Concrete Bond Beam, Cane 

and Mud Roof 

5 to 10 5 to 10 1 EERI (20

A5 Same as A1, with Bamboo or Rope Reinforcement < 5 5 to 10 1 By JudgmRE RAMMED EARTH/PNEUMATICALLY 

IMPACTED STABILIZED EARTH 

< 5 5 to 10 1 EERI (20

RS RUBBLE STONE (FIELD STONE) MASONRY < 5 5 to 10 1 By JudgmRS1 Local Field Stones Dry Stacked (No Mortar). Timber 

Floors. Timber, Earth, or Metal Roof. 

< 5 5 to 10 1 By Judgm

RS2 Same as RS1 with Mud Mortar. < 5 5 to 10 1 EERI (20RS3 Same as RS1 with Lime Mortar. 5 to 10 10 to 20 2 EERI (20RS4 Same as RS1 with Cement Mortar, Vaulted Brick Roof 

and Floors 

< 5 5 to 10 1 EERI (20

66
RS5 Same as RS1 with Cement Mortar and Reinforced 

Concrete Bond Beam. 

5 to 10 10 to 20 2 By Judgm

DS RECTANGULAR CUT STONE MASONRY 

BLOCK 

< 5 5 to 10 2 By Judgm

DS1 Rectangular Cut Stone Masonry Block with Mud 

Mortar, Timber Roof and Floors 

< 5 5 to 10 2 EERI (20

DS2 Same as DS1 with Lime Mortar < 5 5 to 10 2 By JudgmDS3 Same as DS1 with Cement Mortar < 5 5 to 10 2 By JudgmDS4 Same as DS2 with Reinforced Concrete Floors and 

Roof 

< 5 5 to 10 2 By Judgm

UFB UNREINFORCED FIRED BRICK MASONRY 5 to 10 10 to 20 1 By JudgmUFB1 Unreinforced Brick Masonry in Mud Mortar without 

Timber Posts 

< 5 5 to 10 1 EERI (20

UFB2 Unreinforced Brick Masonry in Mud Mortar with 

Timber Posts 

5 to 10 10 to 20 1 EERI (20

UFB3 Unreinforced Fired Brick Masonry, Cement Mortar, 

Timber Flooring, Timber or Steel Beams and Columns, Tie Courses (Bricks Aligned Perpendicular to the Plane of the Wall) 

5 to 10 10 to 20 5 EERI (20

UFB4 Same as UFB3, but with Reinforced Concrete Floor 

and Roof Slabs 

5 to 10 > 20 64 EERI (20

UCB UNREINFORCED CONCRETE BLOCK 

MASONRY, LIME/CEMENT MORTAR 

5 to 10 > 20 8 EERI (20

MS MASSIVE STONE MASONRY IN 

LIME/CEMENT MORTAR 

5 to 10 > 20 13 EERI (20

TU PRECAST CONCRETE TILT-UP WALLS (same 

as HAZUS Type PC1 in Developing and Undeveloped Countries)

5 to 10 > 20 8 By Judgm

INF INFORMAL CONSTRUCTIONS (parts of 

Slums/Squatters) Constructions Made of Wood/Plastic Sheets/Galvanized Iron sheets/Light Metal or Composite etc., not Confirming to Engineering Standards.

< 5 5 to 10 1 By Judgm

67
UNK Unknown Category (Not specified) < 5 5 to 10 1 By Judgm

68
Appendix VII 

Refer Electronic Supplement – Jaiswal and Wald (2008) PAGER Inventory Database v1.3.xls. (This link updated version of this database). 

69
Table 1. Tabulation of the types of data sources, their parameters, assigned quality ratings, and their c

Sr. No. Source of Data Building Stock 

Distribution 

Quality Global Coverage Over

1. World Housing 

Encyclopedia (developed by EERI, USAhttp://www.worldhousing.net/) 

1. Residential: a. Construction type b. Occupancy (D/N/T)

High Medium 

110 residential construction types in 37 countries. Exact fraction of each housing type in a given country is not known. The day and night time occupancy by construction type is available. 

Mediu

2. UN Database (statistical 

database compiled by UN agencies; UN 1993 and UN-HABITAT 2007) 

1. Residential: a. Housing by type of dwelling b. Housing by Construction Material 

Medium Low 

44 countries with construction type description based on external walls and 96 countries with type of housing units. About 110 countries with average occupancy estimated based on total building stock. 

Low 

3. Census of Housing (data 

compiled from housing census statistics) 

1. Residential: a. Housing by type of dwelling b. Housing by construction material c. Occupancy (D/N/T) 2. Nonresidential: a. Dwelling type 

High Medium Low High Medium 

197 countries conducted housing census in 1990. Several countries do not publish housing statistics even though housing census was conducted. 

Mediu

b. Construction type c. Occupancy (D/N/T)

Low 

4. Published Literature

(such as, research articles; 

1. Residential: About 10 countries have been 

identified that contains high 

High 

70
reports; nonproprietary 

information) 

a. Housing by type of dwelling b. Housing by construction material c. Occupancy (D/N/T) 2. Nonresidential: a. Dwelling type b. Construction type c. Occupancy (D/N/T)

High High Low High Medium Medium 

quality information based on conducting survey and verification of other published information e.g., census/ tax assessor’s data. The day and night time occupancy by construction type is not available. 

5. WHE-PAGER Survey

(http://www.worldhousing.net)

1. Residential: a. Construction type c. Occupancy (D/N/T) 2. Nonresidential: a. Construction type b. Occupancy (D/N/T)

High High High High 

Inventory information for about 22 countries has been gathered in the first phase of WHEPAGER expert opinion survey. In order to facilitate their judgment, country-specific inventory information gathered from general internet research and housing censuses was provided to these experts. It is expected that the contributions sought from these expert will help to update the existing PAGER compilations for their respective countries. 

High 

Notes: High quality refers to data compiled from engineering or telephonic surveys, field visits for ground-truth, or data compilations frexperts; Medium quality refers to data compiled by general field surveys and assignments generally not based on engineering standardsdata compiled by non-engineering agencies and was not specifically meant for engineering risk analysis 

71
Table 2. Housing units by construction material of external walls in the year 2004 for 

Tanzania from the UN (2007) database 

Serial 

No. Description of Construction Material (wall) 

Fraction of housing units 

(in percent) 

1. Grass 0.2 

2. Poles and mud 14.7 

3. Sundried bricks 18.7 

4. Baked bricks 13.8 

5. Timber 0.5 

6. Cement bricks 51.0 

7. Stone 1.0 

Table 3. List of countries, source of contact, and web links for countries with housing 

census data 

Serial 

No. Country Source Year Web Link Visit Date 

1 Pakistan 

Pakistan 

Population Census 

Organization 1998 

http://www.statpak.gov.pk/depts/pco/statistics/

other_tables/housing_units_by_construction_

material.pdf 

May 01, 

2007 

2 Philippines 

National Statistics 

Office 2000 http://www.nscb.gov.ph/secstat/d_popn.asp 

May 01, 

2007 

3 Nepal 

Nepal Central 

Bureau of 

Statistics 2004 

http://www.worldbank.org/html/prdph/lsms/co

untry/nepal2/docs/NLSS%20II%20Report%20

Vol%201.pdf 

May 01, 

2007 

4 Japan Statistical Bureau 2003 

http://www.stat.go.jp/english/data/jyutaku/150

3.htm 

May 01, 

2007 

5 Thailand 

National Statistics 

Office Thailand 2000 

http://web.nso.go.th/pop2000/indiregion/whole

tab4.htm 

May 01, 

2007 

6 Mexico 

Housing Study 

Report 1995 

http://www.jchs.harvard.edu/publications/inter

national/SOM_97.pdf 

May 01, 

2007 

7 Australia Geoscience 

Australia 

2007 written communication 

-- 

8 Lithuania Statistics Lithuania 2000 http://www.std.lt Oct. 07 

2007 

9 Iran Statistical Center 

of Iran 

2005 http://www.sci.org.ir Dec. 04 

2007 

72
Table 4. (a) Census data compiled by the National Statistical Office of the Government of 

the Philippines for housing inventory by construction material, (b) Housing Census data 

showing distribution of housing units by construction material (wall and roof construction) 

for Pakistan 

(a) 

(b) 

73
Table 5. List of countries with inventory data from published country-specific resources, 

articles, and reports 

Sr. 

No. 

Country Vintage Data Source 

(direct/inferred) 

1 Albania 2001 2001 Albania Housing 

Census data (inferred/ 

not specific) 

2 Algeria 1983 Petrovski (1983) 

3 Armenia 2007 Expert Opinion 2007 

4 Iran 2005 Ghafory-Ashtiany and 

Mousavi (2005) 

5 Iraq 1983 Petrovski (1983) 

6 Jordan 1983 Petrovski (1983) 

7 Saudi Arabia 1983 Petrovski (1983) 

8 Sudan 1983 Petrovski (1983) 

9 Syrian Republic 1983 Petrovski (1983) 

10 California (U.S.A.) 2002 HAZUS inventory data 

(FEMA, 2006) 

11 United States of America (U.S.A.) 2002 HAZUS inventory data 

(FEMA, 2006) 

12 Italy 2006 Dolce, Kappos and 

others (2006) 

13 Turkey 2002 Bommer, Spence and 

others (2002) 

74
Table 6. Residential construction type distribution for several Middle-Eastern countries, 

from Petrovski (1983) 

Sr. No. Country Description PAGER 

Struct. 

type 

Fraction 

of Resi. 

Buildings 

Traditional construction 

(Adobe, brick or stone masonry) 

UFB 15% 

Modern construction 

(RC/Steel frame with brick infill or combined 

structural system) 

C3L 85% 1 Algeria 

Modern construction with seismic protection C1L 0% 

Traditional construction 

(Adobe, brick or stone masonry) 

A 80% 

Modern construction 

(RC/Steel frame with brick infill or combined 

structural system) 

C3L 20% 2 Sudan 

Modern construction with seismic protection C1L 0% 

Traditional construction 

(Adobe, brick or stone masonry) 

UFB 0% 

Modern construction 

(RC/Steel frame with brick infill or combined 

structural system) 

C3L 100% 3 Saudi Arabia 

Modern construction with seismic protection C1L 0% 

Traditional construction 

(Adobe, brick or stone masonry) 

UFB 70% 

Modern construction 

(RC/Steel frame with brick infill or combined 

structural system) 

C3L 30% 4 Jordan 

Modern construction with seismic protection C1L 0% 

Traditional construction 

(Adobe, brick or stone masonry) 

UFB 60% 

Modern construction 

(RC/Steel frame with brick infill or combined 

structural system) 

C3L 40% 5 Syria 

Modern construction with seismic protection C1L 0% 

Traditional construction 

(Adobe, brick or stone masonry) 

UFB 80% 

Modern construction 

(RC/Steel frame with brick infill or combined 

structural system) 

C3L 20% 6 Iraq 

Modern construction with seismic protection C1L 0% 

75
Table 7. Housing stock distribution based on UN-HABITAT (2007) compiled for Peru 

Sr. No. Description used for defining 

fraction of housing units by 

construction material for outer 

wall (UN-HABITAT, 2007) 

Mapping of 

construction type to 

equivalent PAGER 

Structure Type 

Fraction of total 

residential housing 

stock (the exact 

decimal places of 

estimate are based on 

original data) 

1. 11 Madera W 3.08 % 

2. 12 Piedra con barro RS2 0.12% 

3. 13 Tripley INF 0.93% 

4. 14 Estera W 0.57% 

5. 21 Adobe o tapia A 19.41% 

6. 22 Quincha (cañas con barro) M2 0.94% 

7. 31 Ladrillo o bloque de 

cemento UCB 73.15% 

8. 32 Piedra o sillar con cal o 

cemento DS3 1.33% 

9. 96 Other UNK 0.47% 

Table 8. Housing stock distribution based on the 2000 Census of Population and Housing 

compiled for Australia 

Sr. No. Residence by type of wall 

(Edwards, written 

communication) 

Geoscience 

Australia 

Structural Model 

(Edwards, 

Robinson and 

others, 2004) 

PAGER 

Structure Type 

Fraction of total 

residential 

housing stock 

(the exact 

decimal places of 

estimate are 

based on original 

data) 

1. Double brick URM UFB 52.86% 

2. Brick Veneer W1B W 6.21% 

3. Timber W1 W1 26.07% 

4. Fibro/asbestos cement -NA- INF 14.86% 

76
Table 9. Housing stock distribution based on the 1990 Census of Population and Housing 

compiled for El Salvador 

Sr. No. Residential housing type data of El 

Salvador 

PAGER Structure 

Type 

Fraction of housing 

stock in percentage 

1. Adobe A 1% 

2. Stone (block) masonry DS 10% 

3. Brick masonry UFB 37% 

4. Wood frame with poor infill walls W2 37% 

5. Wood frame with wood panel walls W1 15% 

Table 10. Housing stock distribution based on the Census of Population and Housing 

(2001) compiled for Indonesia 

Sr. No. Residential housing type obtained 

from census data 

PAGER Structure 

Type 

Fraction of housing 

stock in percentage 

1. Unreinforced clay brick masonry 

with timber roof 

UFB3 30% 

2. Stone masonry walls with timber 

roof 

RS1 30% 

3. Mid rise reinforced concrete frame 

with infilled masonry walls 

C3M 20% 

4. Timber frame with mud walls W2 10% 

5. Informal INF 10% 

77
Table 11. Housing stock distribution based on Ghafory-Ashtiany and Mousavi (2005) for 

the city of Bam, Iran 

Sr. No. Residential housing type data for 

Bam in Iran (Ghafory-Ashtiany and 

Mousavi, 2005) 

PAGER Structure 

Type 

Fraction of housing 

stock in percentage (the 

exact decimal places of 

estimate are based on 

original data) 

1. Sun-dried bricks and clay A 53.2% 

2. Brick only UFB 3.5% 

3. Brick and Wood UFB2 1.9% 

4. Brick and Steel UFB6 40.6% 

5. Concrete structures C3L 0.8% 

Table 12. Distribution of residential building stock in Italy 

Sr. No. Description of residential 

housing type data for 

Southern Italy (Dolce, 

Kappos and others, 2006) 

Type PAGER 

Structure 

Type 

Fraction of 

building 

stock 

(by volume) 

Fraction of 

building 

stock 

(by 

number) 

1. Dual, poor design, low RC1I-L C3L 1.8% 4.6% 

2. Dual, good design, low RC1E-L C2L 0.6% 1.3% 

3. Dual, poor design, medium RC1I-M C3M 1.7% 0.8% 

4. Dual, good design, medium RC1E-M C2M 0.4% 0.2% 

5. Dual, poor design, tall RC1I-H C3H 1.4% 0.1% 

6. Dual, good design, tall RC1E-H C2H 0.4% 0.0% 

7. Frame, poor design, low RC2I-L C4L 5.8% 6.5% 

8. Frame, good design, low RC2E-L C1L 5.2% 7.8% 

9. Frame, poor design, medium RC2I-M C4M 27.8% 8.9% 

10. Frame, good design, medium RC2E-M C1M 10.0% 3.4% 

11. Frame, poor design, tall RC2I-H C4H 13.6% 2.0% 

12. Frame, good design, tall RC2E-H C1H 4.1% 0.7% 

13. Stone masonry, low SM-L RS 2.9% 13.6% 

78
14. Stone masonry, medium SM-M RS 5.9% 9.8% 

15. Stone masonry, high SM-H MS 6.6% 2.9% 

16. Brick masonry, low BM-L UFB 4.5% 27.9% 

17. Brick masonry, medium BM-M UFB 4.1% 7.4% 

18. Brick masonry, high BM-H UFB 2.5% 0.6% 

19. Steel structures of any type Steel S 0.4% 1.1% 

20. Other types of buildings Other UNK 0% 0.3% 

Table 13. Distribution of residential building stock in Japan 

Sr. No. Residential housing type data for 

Japan (Statistics Bureau Japan, 

2003). 

PAGER Structure 

Type 

Fraction of housing 

stock in percentage 

1. Wooden W2 31.69% 

2. Wooden and fire-proofed W1 29.68% 

3. Reinforced steel framed concrete C3 31.89% 

4. Steel framed S3 6.44% 

5. Others UNK 0.3% 

Table 14. Distribution of residential building stock in Mexico 

Sr. No. Residential housing 

type 

PAGER Structure 

Type 

Fraction of housing 

stock in percentage 

(1990) 

Fraction of housing 

stock in percentage 

(2000) 

1. Brick/Stone UFB 69.9% 75.7% 

2. Adobe A 14.7% None 

3. Wood W 8.2% None 

4. Other UNK 7.2% 24.3% 

79
Table 15. Distribution of residential building stock in Nepal 

Sr. No. Residential housing type data for Nepal 

(Nepal Central Bureau of Statistics, 2004) 

PAGER Structure 

Type 

Fraction of housing 

stock in percentage 

1. Wood/branches W 18.5% 

2. Mud bonded bricks/stones UFB1 47.5% 

3. Cement bonded bricks/stones and concrete UFB3 18.3% 

4. Others UNK 15.7% 

Table 16. Distribution of residential building stock in New Zealand 

Sr. No. Residential housing type data for New 

Zealand (Dowrick, 1998) 

PAGER Structure 

Type 

Fraction of housing 

stock in percentage 

(by judgment) 

1. Timber buildings W1 78% 

2. Reinforced concrete building C2 8% 

3. Unreinforced brick masonry buildings UFB 7% 

4. Steel buildings S 2% 

5. Reinforced masonry RM 5% 

80
Table 17. Distribution of residential building stock in Taiwan 

Sr. No. Residential housing type data for Nantau and 

Taichung County in Taiwan (Tien, Juang 

and others, 2002) 

PAGER Structure 

Type 

Fraction of housing 

stock in percentage 

1. Mud brick residence A 10.67% 

2. Masonry and reinforced masonry buildings RM 43.75% 

3. Reinforced concrete buildings C3M 37.51% 

4. Composite construction (Steel and RC) C5 0.33% 

5. Steel frames S3 3.63% 

6. Others UNK 4.11% 

Table 18. Distribution of residential building stock in Philippines 

Sr. No. Residential housing type data for Philippines 

(Housing Census, 2000) 

PAGER Structure 

Type 

Fraction of housing 

stock in percentage 

1. Wood W 22.7% 

2. Bamboo/sawali/cogon/nipa (poor-quality 

wood construction in rural area) 

W2 22.8% 

3. Half wood and half concrete/brick/stone 

category 

W 18.9% 

4. Brick, stone, or concrete walls UFB 30.8% 

5. Others UNK 4.8% 

81
Table 19. Distribution of residential building stock in Turkey 

Sr. No. Residential housing type data for Turkey PAGER 

Structure 

Type 

Urban 

(68.2%) 

Rural 

(31.8%) 

Fraction of 

housing 

stock in 

percentage 

1. Timber frame W < 1% < 1% 0.66% 

2. Weak Masonry (adobe, rubble masonry) A 7% 9% 7.64% 

3. Brick/block unreinforced masonry with 

timber floors 

UFB2 19% 26% 21.23% 

4. Brick/block unreinforced masonry with 

concrete floors 

UFB4 17% 21% 18.27% 

5. RC frame with masonry infill – 1 to 3 

stories, poor seismic design 

C3L 44% 

(41.5%) 

36% 39.75% 

6. RC frame with masonry infill – 4 to 7 

stories, poor seismic design 

C3M 4% 3% 3.68% 

7. RC frame with masonry infill – 8 or 

more stories, poor seismic design 

C3H 6% 1% 4.41% 

8. RC frame with masonry infill – 1 to 3 

stories, good seismic design 

C1L 1% < 1% 1.00% 

9. RC frame with masonry infill – 4 to 7 

stories, good seismic design 

C1M < 1% 0 0.34% 

10. RC frame with masonry infill – 8 or 

more stories, good seismic design 

C1H < 1% 0 0.34% 

11. RC shear wall – 4 to 7 stories C2M 1% < 1% 1.00% 

12. RC shear wall – 8 or more stories C2H 2% 1% 1.68% 

13. Single story industrial shed with light 

steel truss roof pinned to cantilever RC 

or steel stanchions 

S3 0% 0% 0% 

14. Single story industrial shed with precast 

concrete roof 

PC1 0% 0% 0% 

82
Table 20. Distribution of California building stock as obtained from the HAZUS (FEMA, 

2006) inventory database 

Sr. No. Building Type HAZUS 

Structure 

Type 

PAGER 

Structure 

Type 

Fraction of 

Buildings 

1 Wood, Light Frame (= 5,000 sq. ft.) W1 W1 91% 

2 Wood, Commercial and Industrial Wood 

(>5,000 sq. ft.) W2 W2 0% 

3 Steel Moment Frame Low-Rise S1L S1L 0% 

4 Steel Moment Frame Mid-Rise S1M S1M 0% 

5 Steel Moment Frame High-Rise S1H S1H 0% 

6 Steel Braced Frame Low-Rise S2L S2L 0% 

7 Steel Braced Frame Mid-Rise S2M S2M 0% 

8 Steel Braced Frame High-Rise S2H S2H 0% 

9 Steel Light Frame S3 S3 0% 

10 Steel Frame with Cast-in-Place Concrete 

Shear Walls Low-Rise S4L S4L 0% 

11 Steel Frame with Cast-in-Place Concrete 

Shear Walls Mid-Rise S4M S4M 0% 

12 Steel Frame with Cast-in-Place Concrete 

Shear Walls High-Rise S4H S4H 0% 

13 Steel Frame with Unreinforced Masonry Infill 

Walls Low-Rise S5L S5L 0% 

14 Steel Frame with Unreinforced Masonry Infill 

Walls Mid-Rise S5M S5M 0% 

15 Steel Frame with Unreinforced Masonry Infill 

Walls High-Rise S5H S5H 0% 

16 Concrete Moment Frame Low-Rise C1L C1L 0% 

17 Concrete Moment Frame Mid-Rise C1M C1M 0% 

18 Concrete Moment Frame High-Rise C1H C1H 0% 

19 Concrete Shear Walls Low-Rise C2L C2L 1% 

20 Concrete Shear Walls Mid-Rise C2M C2M 0% 

21 Concrete Shear Walls High-Rise C2H C2H 0% 

83
22 Concrete Frame with Unreinforced Masonry 

Infill Walls Low-Rise C3L C3L 0% 

23 Concrete Frame with Unreinforced Masonry 

Infill Walls Mid-Rise C3M C3M 0% 

24 Concrete Frame with Unreinforced Masonry 

Infill Walls High-Rise C3H C3H 0% 

25 Precast Concrete Tilt-Up Walls PC1 PC1 0% 

26 Precast Concrete Frames with Concrete Shear 

Walls Low-Rise PC2L PC2L 0% 

27 Precast Concrete Frames with Concrete Shear 

Walls Mid-Rise PC2M PC2M 0% 

28 Precast Concrete Frames with Concrete Shear 

Walls High-Rise PC2H PC2H 0% 

29 Reinforced Masonry Bearing Walls with 

Wood or Metal Deck Diaphragms Low-Rise RM1L RM1L 2% 

30 Reinforced Masonry Bearing Walls with 

Wood or Metal Deck Diaphragms Mid-Rise RM1M RM1M 0% 

31 Reinforced Masonry Bearing Walls with 

Precast Concrete Diaphragms Low-Rise RM2L RM2L 0% 

32 Reinforced Masonry Bearing Walls with 

Precast Concrete Diaphragms Mid-Rise RM2M RM2M 0% 

33 Reinforced Masonry Bearing Walls with 

Wood or Metal Deck Diaphragms High-Rise RM2H RM2H 0% 

34 Unreinforced Masonry Bearing Walls LowRise URML UFB 0% 

35 Unreinforced Masonry Bearing Walls HighRise URMM UFB 0% 

36 Manufactured Home MH MH 6% 

84
85

Table 21. List of countries that provide inventory data obtained during WHE-PAGER 

survey and the name of the experts 

Sr. No. Country Name of Expert 

 1. Algeria Mohammed N. Farsi, Farah Lazzali 

 2. California Craig Comartin 

 3. Chile María Ofelia Moroni 

 4. China 

SUN Baitao/Zhang GUIXIN/Chen Honfu, Institute of 

Engineering Mechanics CEA 

 5. Colombia Luis G. Mejia 

 6. Cyprus Vsevolod Levtchitch 

 7. France THIBAULT Christian 

 8. Germany Sergey Tyagunov, Lothar Stempniewski, Christian Münich 

 9. India C.V.R. Murty 

 10. Indonesia Sugeng Wijanto 

 11. Ireland Robin Spence 

 12. Italy Agostino Goretti 

 13. Japan Charles Scawthorn 

 14. Mexico Sergio M. Alcocer 

 15. Nepal Jitendra Kumar Bothara 

 16. Pakistan Qaisar Ali 

 17. Peru Alejandro Muñoz 

 18. Spain Alex H. Barbat 

 19. Switzerland Kerstin Pfyl-Lang 

 20. Thailand Chitr Lilavivat 

 21. Turkey Polat Gülkan/Ahmet Yakut 

 22. 

United 

Kingdom Robin Spence 
Table 22. WHE-PAGER survey response for Japan, contributed by Dr. Charles Scawthorn from Kyoto 

86
Figure 1. Histogram showing housing units by construction material for Hungary 

87
Data Preparation

Attribute Mapping

Rating

Compilation of 

Single Database 

Through Merging

Phase I

Input

Phase II

Filling the Missing 

Elements by 

Judgment

Phase III

Output

Figure 2. Schematic overview flowchart of the PAGER inventory development 

methodology 

88
Identify A Various Databases

For PAGER Inventory 

Development

Does Can Database Ave. Occupant Info. Include No Select a be Effectively Ave. Occupant Database Determined? Information

No

Yes Yes

Does Can Convert 

Database Geographic Occupancy 

Include No Location be Information to 

Geographic Effectively PAGER Ave. 

Location Determined? Occupancy

Assign “Nil”

Yes Yes

Restructure

Database in

Convert PAGER 

Geographic Processing 

Location to ISO Format

Country Code

Select PAGER 

Attribute for 

Selected Database Does Can (Structure/ Database Occupancy Occupancy etc.) Include No (Resi/Non-resi.) be 

Occupancy Effectively

Data Determined?

Yes Yes Rate PAGER 

Attribute Using Predefined Criteria

Convert Building (High/Medium/Low)

Occupancy to

Resi/Non-resi./

Outside

Process 

Assign “Nil” Selected 

Database For 

Duplicates

Does Can 

Database Construction Data No Include No be Determined 

Construction Type From Other 

Data Indicator?

Are All 

Databases 

Processed ? Yes Yes

Assign “Nil”

Determine Convert Construction Construction Yes Type From Types to PAGER Indicator Structure Types Mapping Compile 

Each

Database

For Further 

Processing A

Figure 3. Schematic flowchart of the PAGER inventory development methodology (Phase 

I – Population of databases and ratings) 

89
Figure 4. Schematic flowchart of the PAGER Inventory Development Methodology (Phase 

II - Merging of Databases) 

90
Figure 5. Map showing geographic coverage by country of the building inventory data 

from different sources aggregated in Phase II 

91
Figure 6. Map showing the housing stock distribution pattern of Iran, identified as a 

possible representative building stock distribution for Afghanistan (where data is not 

available) based on recent vintage and rating of Iran over data from Pakistan 

92
Figure 7. Map showing identification of most representative neighbor for Ecuador from all 

neighboring countries 

93
Figure 8. Map showing the building stock distribution in Armenia, used as a proxy for 

Azerbaijan based on data vintage 

94
Identify Unfilled 

Elements of 

PAGER Inventory 

Matrix 

Select a new Unfilled 

Country or Geographic 

Location

Database 

From 

Phase II

Do 

Multiple Neighbors 

Exist ?

No

Use Estimates from 

Identified Neighbor as 

Proxy Representation for 

Missing Attribute and 

Assign Low Rating

Identify Neighboring Countries Who Share 

Common Boundary (or lie in vicinity in case of 

Islands) with the Missing Country and Have 

Estimates of Missing Attribute

Are All 

Missing Elements 

Replaced For a Given 

Country/Region?

No

Yes

Yes

Are All 

the Unfilled 

Countries/Regions 

Covered?

Final PAGER 

Inventory 

Database To 

Be Used For 

Fatality 

Estimation

Do Multiple 

Neighbors Have Same 

Ratings?

Select a Neighbor 

that has Highest 

Rated Attribute 

Estimate

Select Neighbor 

that has Most 

Recent Vintage 

No

Yes

Yes Does Neighbor 

Exist?

Use Expert Judgment to 

Identify a Neighborhood 

Country that has Highest 

Rating and Most Recent 

Vintage for Missing 

Attribute

No

Yes

Select a Missing Attribute for 

Given Country or Geographic 

Location

No

Figure 9. Schematic flowchart of the PAGER inventory development methodology (Phase 

III – Filling the missing elements) 

95
Figure 10. Photograph showing typical rural house in Bangladesh made of straw, mud, 

and timber frame 

96
Figure 11. Photograph showing general housing stock in Thailand (Bangkok) obtained 

from general Internet search 

97
Figure 12. Bar chart showing housing stock distribution for Peru (United Nations, 1993) 

98
Figure 13. Photograph showing typical residential building stock in Peru constructed 

using brick masonry construction 

99
Distribution of urban population by fraction of residential 

structure types in urban areas of Peru

C3, 20%

C1, 10%

C2, 20%

UFB3, 25%

RM2, 20%

M, 5%

Figure 14. Pie chart showing distribution of housing stock in urban areas of Peru obtained 

from WHE-PAGER expert survey 

100
Urban Area

16%

5%

56%

9%

4%

10%

Metal skeleton (S)

Reinforced concrete (C3)

Brick and steel or stone and

steel (UFB4)

Brick and wood or stone and

wood (UFB3)

Cement block (UCB)

Not Stated (Adobe/Informal) (A)

Rural Area

1% 1%

26%

24%

9% 39%

Metal skeleton (S)

Reinforced concrete (C3)

Brick and steel or stone and

steel (UFB4)

Brick and wood or stone and

wood (UFB3)

Cement block (UCB)

Not Stated (Adobe/Informal) (A)

Figure 15. Pie chart showing distribution of housing stock in urban and rural areas of Iran 

obtained from housing census data (Iran Statistical Year Book, 2005) 

101
Figure 16. Photograph showing typical multistoried reinforced concrete apartment 

buildings with masonry infill walls in Ankara, Turkey 

102
Figure 17. Map showing global coverage of PAGER building inventory database with data compiled fr

103