Estimation of Redevelopment Probability using Panel Data

CSIS Discussion Paper.104 (The University of Tokyo)

Estimation of Redevelopment Probability using Panel Data*-Asset Bubble Burst and Office Market in Tokyo-

Chihiro SHIMIZU†
Koji KARATO‡
28. January.2010

Summary
Purpose: When Japan’s asset bubble burst, the office vacancy rate soared sharply. This study
targets the office market in Tokyo’s 23 special wards during Japan’s bubble burst period. It aims
to define economic conditions for the redevelopment/conversion of offices into housing andestimate the redevelopment/conversion probability under the conditions.
Design/methodology/approach: The precondition for land-use conversion is that subsequentprofit excluding destruction and reconstruction costs is estimated to increase from the present
level for existing buildings. We estimated hedonic functions for offices and housing, computed
profit gaps for approximately 40,000 buildings used for offices in 1991, and projected how the
profit gaps would influence the land-use conversion probability. Specifically, we used panel data
for two time points in the 1990s to examine the significance of redevelopment/conversion
conditions.
Findings: We found that if random effects are used to control for individual characteristics ofbuildings, the redevelopment probability rises significantly when profit from land after
redevelopment is expected to exceed that from present land uses. This increase is larger in thecentral part of a city.
Research limitations/implications: Limitations stem from the nature of Japanese data limited tothe conversion of offices into housing. In the future, we may develop a model to generalize
land-use conversion conditions.
Originality/value: This is the first study to specify the process of land-use adjustments thatemerged during the bubble burst. This is also the first empirical study using panel data to analyse
conditions for redevelopment.
Key words: hedonic approach, random probit model, urban redevelopment, Japan’s asset bubble
Paper type: Research paper
JEL Classification : C31 - Cross-Sectional Models; Spatial, R31 - Housing Supply and Markets*

This research is a part of the project entitled: Understanding Inflation Dynamics of the Japanese Economy, funded by JSPS
Grant-in-Aid for Creative Scientiic Research (18GS0101), headed by Tsutomu Watanabe. During the study, we received relevant
comments from Yoshitsugu Kanemoto, Fukuju Yamazaki, Yoshihisa Asada and Yasushi Asami. We also received indispensible
research data from the Bureau of Planning at the Tokyo Metropolitan Government. We sincerely thank them for their help.
†
Associate Professor, International School of Economics and Business Administration, Reitaku University
Hikarigaoka2-1-1, Kashiwa-city, Chiba, 277-8686 Japan , Tel. +81-(0)4-7173-3439, Fax. +81-(0)4-7173-1100,
e-mail: cshimizu@reitaku-u.ac.jp‡
Associate Professor, Faculty of Economics, University of Toyama

1Estimation of Redevelopment Probability using Panel Data

1. Study Objectives
Sharp real estate price hikes and declines, or the formation and bursting of real estate
bubbles, have brought about serious economic problems in many countries.
Japan in particular experienced fast real estate price hikes and declines between the
mid-1980s and mid-1990s. The hikes were described as the largest real estate bubbles in the 20th
century. After the bubble burst, Japan saw a long economic slump labelled a “lost decade”. What
happened in the Japanese real estate market in the bubble formation and burst process? How did
the microstructure of the real estate market change amid the large macro fluctuations of real estate
prices?
While speculative real estate transactions were repeated in urban areas during theso-called bubble period, undesirable land-use conversions came under fire. In a typical case,urban centre houses were converted into small office buildings called “pencil buildings”. Even in
suburban areas, large office buildings and commercial facilities were constructed.
After the bubble burst, vacancy rates soared for many office buildings with massive realestate assets left idle, even in central Tokyo with the highest economic concentration in Japan.
An economic explanation of the phenomenon is that the distribution of land resources
was distorted within the metropolis, bringing about strong inefficiencies. When inefficiencies
exist in the land-use market, conversion/redevelopment is required to achieve a new equilibrium.
This means that land-use inefficiencies are resolved through conversion to optimize the
distribution of resources anew.
Earlier studies analysed land-use adjustment processes, treating them as urban
redevelopment problems and specifying conditions for redevelopment.
The dynamic urban redevelopment model by Wheaton (1982) assumes that housing
stock developed at one point in time exists at multiple time points. Capital costs emerge at thedevelopment point and are sunk at other points. Therefore, rents after the development are
different from those upon the development and are based on housing stock after the development.
Redevelopment is implemented when post-redevelopment rent income minus capital costs for theredevelopment is projected to exceed the level based on the existing housing stock.
Rosenthal and Helsley (1994) used an empirical analysis to verify Wheaton’s conditions
for redevelopment. Using a structural probit model considering a selection bias, they found that
housing redevelopment is implemented when the price of the land for the redevelopment is
projected to exceed the existing land price plus housing capital destruction costs.
Munneke (1996) used the Rosenthal and Helsley empirical analysis framework for2

Estimation of Redevelopment Probability using Panel Data

commercial real estate. This study also indicates that the redevelopment probability rises as land
prices after redevelopment are projected to increase from present levels. McGrath (2000)
conducted an empirical analysis of commercial real estate by considering redevelopment
conditions while taking into account soil pollution risks of land for redevelopment.
These studies used data at a given point in time for analyses. They apparently targeted
snapshots of the Wheaton model as temporary economic conditions to consider the advisability ofredevelopment conditions. These empirical analyses are based on cross-section data at a point intime and may fail to identify individual characteristics of lands or buildings in a city.
In this study we observe rental office and housing rents and building use conversions,define economic conditions for the redevelopment/conversion of buildings and estimate theredevelopment/conversion probability under these conditions. Specifically, we use panel data forbuildings at two time points in the 1990s to examine the significance of conditions for converting
offices into housing while controlling effects of individual characteristics.
The following are reasons for our focus on the switch from the office to the housing
market. First, profit from offices is generally higher than that from housing in urban centres.
Therefore, small houses are usually bundled for office buildings. The conversion of offices into
housing apparently occurs after landowners acknowledge land-use failures and closely examine
profitability of land when it is used for office buildings and for housing.
Second, we can ignore variables of urban planning constraints in the office-to-housing
conversion case. We are interested in the probability of land-use conversion in the case where
profit gaps emerge between land uses. In residential zones, however, urban planning constraints
frequently prevent housing lands from being used for other purposes, but no legal regulations
exist to affect the conversion of office buildings into housing in urban planning areas where suchbuildings exist. Therefore, land-use conversion can depend on economic reasons alone.
Finally, we can ignore land intensification costs. In urban areas, housing lands are
frequently fragmented. High costs for their intensification have frequently impeded land-use
conversions and land mergers. However, any such costs may not necessarily have to beconsidered for the conversion of office buildings into housing.
For the three reasons above, we looked at the office market of Tokyo’s special wards,
which are known to be some of the most concentrated cities in the world, and compared profit
from offices with that from housing and estimated how the profit gap influenced the land-use
conversion probability. The estimation covered the decade between 1991 and 2001. By coveringsuch a long period, we can expand our research into a panel data analysis. Therefore, we canextract unobservable characteristic differences among lands or buildings as individual effects.

3

Estimation of Redevelopment Probability using Panel Data

This is another advantage of the estimation.
Furthermore, the period covers the peak and burst of the real estate bubble that began in
the second half of the 1980s. Our research into changes in the microstructure within the cityfollowing these developments may be very significant for macroeconomic as well as urban
policies.

2. Conditions for Redevelopment and Econometric Model
Here, all reasonable landowners are assumed to choose land uses that can generate thehighest profit given the efficiency of the land market. This study targets Tokyo’s special wards,
featuring high urban concentration, and establishes a model limiting land uses to offices andhousing to simplify the problem1. Specifically, this study observes the conversion of offices into
housing during real estate price drops after the bubble burst in the early 1990s and analyses
economic conditions that prompt landowners to eliminate or redevelop buildings for theconversion of offices into housing.
This can be depicted through landowners’ land development behaviours. Capital K andconstant land area L are invested to produce a building with a total floor space of Q . This can beindicated by the production function of Q  F K , L  . In a bid to construct a new building, thelandowner destroys the existing building at a cost of c per floor area. Given the discount rate iand the rent R R for floor area Q , the maximized profit per land area for the new building for
housing can be indicated by the following equation:

max r R KR R F K , L   iK  cQ
.
L

(1)

If the total floor area of the existing office building is Q  F K , L  , profit per land area from theoffice building in the absence of redevelopment is r C  R C  Q L  . Therefore, an incentive forthe redevelopment emerges for the r R  r C  0 case:
R R F K , L   iK  cK  R C Q  0 .

(2)

1
In the actual land market, land-use regulations exist to control land-use externalities. While strong regulations exist for plants andurban farmlands, regulations are weak concerning the conversion of offices into housing, even with regulations in regard to building
standards. Therefore, we believe that the assumption here is realistic.

4

Estimation of Redevelopment Probability using Panel Data

If the production function is specified as F K , L   AK  L  , the optimization condition in
Equation (1) is indicated by R R F K , L  K   i . Therefore, the redevelopment condition (2)can be rewritten as follows.
  1    R R Q  R C  c Q  0(3)With this condition, we use the binary choice model with panel data to analyse
empirically whether the redevelopment decision can be explained by a differential between
profits before and after the redevelopment. The estimation model is as follows~
it   it  uit
uit     i   iti  1,2,, n t  1, 2

(4)

where uit indicates the error component,  represents coefficient of the common constant term,

i denotes each group’s random effect, it is a random variable according to the standard normal
distribution assuming an average of zero and a variance of 1. The relevant land unit may be~~

redeveloped in the it  0 it  1 case or left to continue with the present use in the it  0it  0 case. Therefore, the redevelopment probability can be indicated by the following
equation.

~Pr it  1  Pr it  0   Pr  it    it     i     it     i (5)The equation (5) may be maintained provided the density function depicting the  distributionfunction is symmetrical around zero. When a land unit is redeveloped because its projected rent islarger than the combination of its present rent and redevelopment costs,  is expected to exceed
zero. Hereafter, we use a panel probit model to estimate Parameter  and the random effect(based on Baltagi, 2008).

3. Data

3.1. Land uses and use conversions

This study uses the building-based GIS (geographical information system) polygon data5

Estimation of Redevelopment Probability using Panel Data

from a land and building use survey by the Bureau of Planning of the Tokyo Metropolitan
Government to observe how buildings that existed in 1991 were redeveloped and converted by
1996 and 2001. The number of office buildings in 1991 stood at 40,516, excluding those used forboth stores and housing. Figure 1 indicates their distribution.
Of the 40,516 office buildings that existed in 1991, 2,607 were redeveloped or converted
into housing by 1996, with the remaining 37,909 buildings used still for offices. Of officebuildings that existed in 1996, 3,576 were redeveloped or converted into housing by 2001. The
remaining 36,940 office buildings remained as offices. Details of the panel data are described in
Section 4.3.

3.2. Office and housing rents

Office rent RR and housing rent RC must be specified to estimate the model in the previous
section. We used rent data provided by the National Federation of Real Estate Transaction
Associations, known as Zentakuren, for the period between January 1991 and December 2004.
The data covered 13,147 rent contracts during the period.
Meanwhile, we used “Weekly residential listing magazine—Rental Homes” from Recruit
Co. to collect housing rent data2. From the data in the magazine, we selected data that weredeleted because contracts had been concluded. Rent prices upon their deletion from magazines
represent first offers in the reverse auction process where landlords send house quality and rent
information through magazines and continue to cut rents until they find tenants. These figures canbe characterized as the highest prices for tenants, but may be taken as market prices because few
tenants successfully negotiate reductions in rents from offered levels 3 . Between 1991 andDecember 2004, there were data on 488,348 rents. Office and housing rent databases are shown in
Table 1 and a statistical summary in Table 2.
Each database covers a 14-year period in which bubbles formed and burst, including
volatile rent data. The average office rent was 4,851 yen per square metre with a standard
deviation of 1,925 indicating strong volatility. The average housing rent was 3,248 yen per square
metre with a standard deviation of 8244.
We use the above data to estimate floor rents. Office rents are determined throughbusiness location, based on the convenience of business communications and employees’

2

Steel apartments account for most rental housing stock. Because our study was designed to compare housing with office buildings,
however, we limited data for our analysis to RC (reinforced concrete) and SRC (steel reinforced concrete) buildings.
3
On a weekly basis, Recruit monitors whether contracts are concluded on advertised rents and how rents failing to meet tenantrequests are lowered. As a result of monitoring, it has been found that final rent levels offered just before their deletion from themagazine are equal to contract levels (as confirmed with data from the period 1996 to 2001).
4
Office rents ranged from the minimum at 1,815 yen to the maximum at 13,310 yen and housing rents from 600 yen to 13,300 yen.
Both rents were distributed in the same area.

6

Estimation of Redevelopment Probability using Panel Data

commutation as well as workplace conditions such as space. Because the data for this study cover
14 years from 1991 to 2004, we make next pooling regression model as follows:log RitO  xiO ' O  d iO ' O  vitO

(6)

where RitO stands for office rent per unit of floor area for property i at time t, xi for the vector of
property i characteristics (including floor space, distance to the nearest station, age, proximity toan urban centre, and a regional dummy variable), O for the relevant implicit price vector, d iO fora time dummy variable vector that takes the value of 1 at time t and 0 at any other time, O for thetime effect vector, and vitO is the disturbance term.
Housing rents are assumed to be based on commutation convenience, indicated byproximity to the urban centre, the distance to the nearest station, building age, structure and othercharacteristics such as window and door aspects. Prices of one-room apartments mainly forsingles, compact houses for DINKS (double income no kids) and other small families, andfamily-type houses for large households are structurally different. Housing preferences for small
households including singles and DINKS are different from those for large households including
parents and their children. Therefore, their bid prices are structurally different (Shimizu et al.,2004). The model is given in next equation:log RitH  xiH '  H  d iH '  H  vitH

(7)

where RitH stands for the housing rent per unit area for property i at times t and xi for the vector of
property i characteristics (including space occupancy, age, distance to the nearest station, time tothe urban centre and a regional dummy variable).

3.3. Capital share and destruction costs

To specify  it for profit differential in equation (4), we must estimate capital share  forfloor space production and cost c (per unit area) for the demolition of the existing building.
However, the land and building use survey by the Bureau of Planning at the Tokyo Metropolitan
Government that we use for this study include no data on capital investment or demolition costs
while indicating changes in use.
Therefore, we use another data set to estimate  and c . Nihon no Toshi Saikaihatsu(Japan’s Urban Redevelopment) by the Urban Renewal Association of Japan records construction
7

Estimation of Redevelopment Probability using Panel Data

plans and costs for buildings redeveloped between 1982 and 2001. It includes 107 cases in Tokyo.
Based on this source, Table 3 indicates the descriptive statistics for the total floor space ofredeveloped buildings, real construction costs calculated with the consumer price index (100 forthe base year of 2000) and the total site area.
We use data in Table 3 to logarithmically transform the Cobb–Douglas Production
Function Q  AK  L to estimate parameters. Here, Q stands for the total floor space, K forconstruction costs and L for the site area size. Respective dummy variables for special wards ofTokyo are introduced to control for individual regional effects. Years for completion are used as
trend variables to control for time effects. Table 4 indicates ordinary least squares estimation,
which allow us to compute the capital share of floor space production at   0.390.
A fact-finding survey on building expansion, reconstruction and refurbishment (by the
Ministry of Land, Infrastructure, Transport and Tourism) has reported the average building
destruction cost as 14,394 yen per square metre. If the yield on the benchmark 10-year
government bond issue is used as a discount rate at time of completion, the demolition cost at agiven point in time is defined as c = discount rate ×14,394 yen.

4. Estimation Results

4.1. Rent functions for office and housing uses

Table 5 indicates estimation results for office and housing rent functions. Regarding theoffice rent function, age, or number of years since construction, was estimated at –0.093 and the
distance to the nearest station at –0.219. As for age, the rent per square metre was estimated to
decline by 9.3% every year. Although the decline appears too fast, the age variable apparently
accounts for fast economic and technological deterioration of old office buildings amid theadvancement of office buildings (for adaptations to office automation equipment, higher ceilingsand earthquake resistance) and building methods (for features such as columns). Given the
average age of 16 years for buildings in our analysis, we believe that the tendency may be strong.
Distance to the nearest station indicates how business communication and workers’commutation is convenient.
Regarding the housing rent function for the standard compact type, age is estimated at–0.070, distance to the nearest station at –0.034, the First-floor dummy(Table.1) at –0.042 andproximity to the urban centre at –0.066. All of these variables are negative, consistent with thegeneral tendency, but space occupancy is given as –0.197 in contrast to a positive figure for theoffice rent model. We must pay attention to the sign difference. Here, the constant term dummy
8Estimation of Redevelopment Probability using Panel Dataand the cross term are also observed.
Among constant term dummies, the one-room dummy is estimated at +0.706 and thefamily-type dummy at –1.581. Regarding cross terms between the one-room dummy and thevariables, space occupancy is estimated at –0.263, distance to the nearest station at –0.011, age at+0.025 and time to the urban centre at –0.040. The estimation results indicate that the tendency to
avoid age for the one-room type is weaker than for the compact type. One-room apartment
residents may give priority to convenience rather than environment, demonstrating strong
preferences for shorter distance to the nearest station and a shorter time to the urban centre.
For the family-house type, space occupancy is estimated at +0.043, distance to thenearest station at +0.004, age at –0.002 and time to the urban centre at –0.035. Family-type house
residents have stronger preferences than compact or one-room house residents for newer andwider buildings. If +0.004 of the cross term is taken into account, the distance to the nearest
station is then –0.030. This tendency indicates that residents in relatively wide rental
condominiums give less priority to traffic convenience than do one-room and compact house
residents. This suggests that better residential environments for houses are associated with longer
distances to the nearest station. In Tokyo’s special wards, railway stations and their vicinities
feature greater convenience and commercial concentration while lacking greenery, playgrounds
or security. At locations that are more distant from railway stations, the natural environment, parkdevelopment and security may be better. This may be interpreted to mean that households inlarger houses might have given priority to natural environmental quality rather than convenience
associated with shorter distances to railway stations.
Therefore, family-type house residents who demonstrate stronger preferences for better
residential environments are expected to be less sensitive to distances to railway stations than
one-room or compact house residents.

4.2. Condition for profit gaps

The above office and housing rent function parameters are used to measure theoreticalrents for buildings in our analysis. Building data identified through the GIS polygon include use,
floor-space ratio, area, number of floors, building shape and geographic coordinates. Includingtime effects, these data are included to compute theoretical (predicted) office and housing rents
for each building in 1996 and 2001.
First, we computed theoretical rents, compared theoretical office and housing rents andconfirmed the distribution of buildings that should be converted into housing for higher rents. The
distribution of buildings for which housing rents would be higher than office rents is given for9

Estimation of Redevelopment Probability using Panel Data

1991, 1996 and 2001 (Figures 2, 3 and 4). In 1991, there are few buildings for which housing
rents were higher than office rents, but such buildings proliferated year by year as bubbles burst.
Particularly, clear distribution biases were confirmed. Such rent or profit gaps do not lead
immediately to building use conversions because these are accompanied by demolition andreconstruction costs. If land-use conversions are expected to improve profit even with these costs
taken into account, incentives for conversions may be effective. If land-use conversions aretemporarily projected to improve profit at a certain point, however, they may not necessarily be
implemented. Because real estate properties are durable investment goods, land-use conversions
may not be implemented unless net profit is expected to improve even with costs taken into
account for a certain period of time. Therefore, we computed five-year average profit gaps5 andtook destruction and reconstruction costs into account in the following way. Here, we use thecapital investment share (   0.390) and the marginal destruction cost ( c = discount rate × 14,394
yen) to compute  i ,1996 ,  i , 2001 . These panel data are given in Table 6.

 represents a profit gap, or a difference between office and housing rents. According to
the 1991 descriptive statistics, the number of buildings subjected to redevelopment/conversion
came to 2,607, accounting for 6% of the total. The profit gap for redevelopment cases is comparedwith that for non-redevelopment cases as   1    0 . This indicates that the gap islarger for redevelopment cases. In 2001, redevelopment/conversion covered 3,576 buildings or
3% of the total. The profit gap was larger for redevelopment cases. Estimation results in Table 5
indicate that both office and housing rent indices declined between 1991 and 2001. The profit gap
in the period was negative. Even in the period, opportunity costs for avoiding redevelopment
indicates that the profit gap for buildings subjected to redevelopment was larger than that for
those left unchanged.

4.3. Random probit model estimation results

Table 7 displays results of the random probit estimation using the above data for Equation(4). The estimated coefficient of the profit gap is significantly positive, indicating that theprobability of redevelopment increases when profit from redevelopment is expected to rise. represents the standard error of the random effect.    2 1   2 is the correlation coefficientfor i  it and i  is , indicating the degree of panel-level dispersive elements’ contributions
to the dispersion of the entire model. The estimators of a random effect are significantly different
from pooling estimation results, since the standard errors of correlation coefficient  in brackets
5)

We used the 1986–1990 average gap between office and housing rents for the 1991 analysis, the 1991–1995 average for the 1996
analysis and the 1996–2000 average for the 2001 analysis.

10Estimation of Redevelopment Probability using Panel Data

are sufficiently small. The Wald statistic tests the null hypothesis that the estimated coefficient of
 is zero. The null hypothesis is rejected.
We also defined the following regions to find redevelopment effect gaps among theregions:
Region 1: Chiyoda, Chuo and Minato Wards
Region 2: Shinjuku, Bunkyo, Taito, Shinagawa and Shibuya Wards
Region 3: Sumida, Koto, Ota, Meguro, Nakano, Toshima, Arakawa, Setagaya, Suginami,
Nerima, Itabashi, Kita, Adachi, Katsushika and Edogawa Wards
Samples were divided into the three regional groups for estimation. Estimation results areshown in the second, third and fourth rows of Table 7. For region 1 in the second row, theconversion probability is estimated only for the central Tokyo region covering Chiyoda, Chuo andMinato Wards. Region 2 in the third row and region 3 in the fourth row surround the central
region. For all samples, estimated coefficients of profit gaps are positive and significant withstandard errors being sufficiently small. Estimated coefficients in regions 2 and 3 are larger thanthose in region 1, indicating that offices in the non-central regions would have a greater
probability of being converted into housing than those in the central region under the same profit
conditions.
Spikes and plunges in office rents in the 1990s boosted the office vacancy rate todeteriorate the profitability of office buildings in the non-central regions more sharply than in thecentral region. A factor behind the deterioration was that houses were aggressively converted into
offices under strong expectations of higher office rents amid swelling bubbles in the 1980s. We
assume that land use was changed again to compensate for the past-development failure.
Eventually, our analysis has empirically demonstrated the assumption.

5. Conclusion

In this study, we observed rental office and house rents and building use conversions,defined economic conditions for the redevelopment/conversion of buildings, and estimated theredevelopment/conversion probability under these conditions. While earlier studies have been
limited to single time-point cross-section analyses, this study has used panel data at two time
points in the 1990s to examine the significance of the redevelopment/conversion conditions while
controlling for effects of individual land/building characteristics. This examination is the key
contribution of this study. By limiting our analysis target to the conversion of offices into housing,
we have measured the pure effects of profit gaps on land-use conversion without the need to11

Estimation of Redevelopment Probability using Panel Dataconsider land use and other legal regulations.
The 1990s saw a fast shrinkage of land and rental office and housing markets in theJapanese economy. Office rents declined faster than housing rents amid the economic downtrend
in the decade, indicating that landlords might have paid huge opportunity costs by maintaining
office buildings. In particular, office rent spikes and plunges were sharp in the central Tokyo
region. Rapid changes in economic conditions may have prompted economic units to alter theiroperations. Our analysis, where samples were divided into three regional groups, indicated thatgrowing profit gaps triggered redevelopment more strongly in the non-central regions than in thecentral region.
Many industrial countries have experienced phenomena indicating that economic
confusion resulting from the generation and burst of real estate bubbles can develop into serious
economic problems. A real estate market recovery is frequently accompanied by land-use
conversions and takes considerable time. In the bubble period, land intensification and land-use
conversions may be conducted proactively, distorting the distribution of resources. It is not an
exaggeration to say that no true real estate market recovery may come unless such resource
distribution distortions are corrected. The correction might easily be expected to take more timeamid real estate price declines than amid spikes. Japan’s real estate price decline after the bubble
period, described as the “lost decade”, took a long time and caused an extended economic slump.
Slow land-use conversions to correct market distortions may have been a factor behind theprolonged slump.
Our study focused on land-use conversions that emerged to correct distortions in
distribution of resources amid dynamic real estate market volatility including the generation andburst of bubbles and specified factors affecting such conversions. In the most typical land-use
conversions, however, houses generating low income may be converted into offices yielding a
relatively high income. As a matter of course, we must consider the conversion of houses into
offices as well as the reverse to generalize the land-use adjustment process. We would like toaddress this question in the future.

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Estimation of Redevelopment Probability using Panel Data

References:

Baltagi, B.H. (2008), Econometric Analysis of Panel Data 4th ed., John Wiley & Sons Ltd.
McGrath, D.T. (2000), “Urban industrial land redevelopment and contamination risk”, Journal of
Urban Economics, 47, 414–442.

Munneke, H.J. (1996), “Redevelopment decisions for commercial and industrial properties”,
Journal of Urban Economics, 39, 229–253.

Rosenthal S.S. and Helsley, R.W. (1994), “Redevelopment and the urban land price gradient”,
Journal of Urban Economics, 35, 182–200.

Shimizu, C., Nishimura K.G., and Asami, Y. (2004), “Search and vacancy costs in the Tokyo
housing market: An attempt to measure social costs of imperfect information”, Regional and
Urban Development Studies, 16, 3, 210–230.

Wheaton, W.C. (1982), “Urban spatial development with durable but replaceable capital”,
Journal of Urban Economics, 12, 53–67.

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Estimation of Redevelopment Probability using Panel Data

Table 1. Rent Data Outline

Symbols

Variables

WK

Distance to nearest station

Contents
Time to the nearest station (walking and bus).

Unit
minutes

Average of railway travel time in daytime to the mostAccessibility to central
crowded 40 stations in 1988 weighted by the number

ACC

minutesbusiness districtof passengers at the stations*.
FS

Floor space/ square metres

Floor spacem2

Total floor spacem2

Total floor space/ squareTA
metresPeriod between the date when the data are deleted
Number of years after
from the magazine and the date of construction of the

BY

year

constructionbuilding.
BS

Balcony space/ square metres

NU

Number of units

Balcony space (as shown in Jutaku Joho magazine).

m2

Total number of units in the condominium.

unit

Period between the date when the data appear in theRT

Market reservation time

magazine** for the first time and the date when thedatedata are deleted
YEN/
MCManagement cost

Management fee.
month
Whether the travel time includes time on bus 1,

WD

Walk dummy

not including time on bus 1 but including time on bus

(0,1)

0.
The property is on the ground floor 1,
FF

First-floor dummy(0,1)
on other floors 0.
The property is on the top floor 1,

HF

Highest floor dummy(0,1)
on the other floors 0.
Windows facing south 1，

SD

South-facing dummy(0,1)
other directions 0.

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Estimation of Redevelopment Probability using Panel Data

Fenestrae facing south, south west or south east 1,
SD2

South-facing dummy2(0,1)
other directions 0.
Steel reinforced concrete frame structure 1,

TK

Ferroconcrete dummy(0,1)
other structure 0.

Housing Loan Corporation

Eligible for Housing Loan Corporation loan 1,
(0,1)

KD
dummy

not eligible 0.
j th administrative district 1,

LDj (j=0,…,J)

Location (Ward) dummy(0,1)
other district 0.
i th railway line 1, other railway line 0.

RDk
Railway line dummy(0,1)(10 railway lines appeared in the magazine)(k=0,…,K)k th month 1,
TDl (k=0,…,L)Time dummy (monthly)(0,1)
other month 0.

* Shinjuku station is the busiest station. The busiest 40 stations include main terminal stations of the Yamanote Line suchas Shinagawa, Ikebukuro and Shibuya as well as Yokohama, Kawasaki, Chiba, Omiya and Kashiwa stations. We haveestablished a 73,920 railway line network database, which is worked out of 1,848 stations that appeared in the magazine
for the 40 stations. This database is updated every six months.
** Weekly residential listing magazine by Recruit Co.

15

Estimation of Redevelopment Probability using Panel Data

Table 2. Descriptive Statistics of Office and Housing Rent Data

OfficeHousingStandard

AverageStandard
Averagedeviation

deviation

Rent (yen/m2)

4,851.48

1,925.12

3,248.26

824.90Contractual space (m2)264.02309.87

41.03

20.63

Distance to Tokyo centre (minutes)

12.46

6.25

10.53

7.17

Number of years after construction (years)

16.19

10.29

9.26

7.28

Distance to station (minutes)

4.13

2.91

6.76

3.89

Total floor space (m2)

3,426.36

4,520.41––

Number of observations=

13,147488,348

Table 3. Redevelopment Data (Number of observations at 107)

Standard
Average

Minimum

Maximum

deviation
Q

Total floor space (m2)42,327.9

49,338.5

1,536.0

360,600.0

198.4

222.2

2.9

1,042.6

7,311.7

6,827.9

626.0

48,729.0

Real construction costK(million yen)L

Site space (m2)

Source: Nihon no Toshi Saikaihatsu (Japan’s Urban Redevelopment) Vol. 1–6, Urban Renewal Association of Japan.
The real construction cost was calculated by deflating nominal values with the consumer price index for which the baseyear is 2000.

16

Estimation of Redevelopment Probability using Panel Data

Table 4. Floor Space Production Function

coef.

t-valueConstant term

24.140

2.673

log K

0.390

10.704

log L

0.670

15.077

Annual trend−0.011−2.396

Ward dummy

Yes

Adj. R2

0.959

Note: The annual trend indicates an estimated coefficient of the trend term representing the time of completion.

17

Estimation of Redevelopment Probability using Panel Data

Table 5. Office and Housing Rent Function Estimation Results

Method of Estimation

OLS

Dependent Variable

OR: Rent of Office (in log)

Property Characteristics (in log)

RC: Rent of Condominium (in log)

Coefficientt-value

Coefficientt-valueConstant

8.374

181.483

0.253–24.999

FS: Contractual space

0.190

59.102–0.197–141.297–0.093–24.174–0.070–259.324

WK: Distance to nearest station–0.219–46.556–0.034–70.827

ACC: Time distance to Tokyo centre–0.112–25.362–0.066–117.539

TA: Total floor space

0.051

16.932––

SRC: SRC building dummy

0.199

34.0200.013

29.494

D1F: First-floor dummy–––0.042–76.386

DR1: One-room dummy

–

–

0.706

94.008

DRF: Family-type dummy–––1.581–125.536

DR1×FS–––0.263–123.852

DR1×WK–––0.011–14.917

DR1×BY

–

–

0.025

63.409

DR1×ACC–––0.040–74.509

DRF×FS

–

–

0.403

137.089

DRF×WK

–

–

0.004

4.966

DRF×BY–––0.002–3.705

DRF×ACC–––0.035–46.599

BY: Number of years after
construction

Cross-Term Effect by Property Characteristics

Ward (city) Dummy

Yes

Yes

Railway/Subway Line Dummy

Yes

Yes

Time Dummy

Yes

Yes

Adjusted R square=

0.608

0.758

Number of observations=

13,147488,348

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Estimation of Redevelopment Probability using Panel Data

Table 6. Panel Data Outline

Number ofYear

Variable

Unit

Standard
Averageobservations

Minimum

Maximum

deviation

RR

Yen

40516

8399

2836

2837

26542

RC

Yen

40516

4720

765

3018

6451

40516

−10.91

55.11−2712.34

−0.01

40516

0.06

0.25

0

1



2607

−2.25

7.07

−153.73−0.01

37909−11.51

56.90−2712.34

−0.01

1996



Million
yen



–

RR

Yen

40516

6402

2162

2163

20232

RC

Yen

40516

4808

779

3073

6570

40516

−7.19

37.66

−1878.82

0.02

40516

0.09

0.28

0

1



3576

−1.44

4.21

−101.270.00

36940

−7.75

39.38

−1878.82

0.02

2001



Millionyen–Note: RR indicates a rent after redevelopment and RC a rent for a case without redevelopment.

19

Estimation of Redevelopment Probability using Panel Data

Table 7. Probit Estimation of Redevelopment Probability

All samples

Region 1

Region 2

Region 3

0.31810.05760.44470.3407(0.0093)(0.0058)(0.0250)(0.0219)−13.5617−5.7765−9.3597−9.7961(0.4317)(0.1630)(0.5139)(0.6578)

10.50112.98837.64788.0016(0.3327)(0.0903)(0.4046)(0.4998)

0.99100.89930.98320.9846

(0.0006)(0.0055)(0.0017)(0.0019)

Number of obs.

81032

30110

19898

30468

Individual number of groups

40516

15055

9949

15234

1160.1

98.8

315.3

242.8

[.000][.000][.000][.000]−15071.5−2567.9−3792.0−8043.3

Constant

Wald (chi squared)
Log likelihood

Note. In parentheses are standard errors. Explained variables are binary variables: 1 for redevelopment cases and 0 for
non-redevelopment cases.  is the correlation coefficient of the error structure including random effects. Wald
represents a test static (chi squared at freedom degree of 1). Numbers in brackets indicate the probability.

20

Estimation of Redevelopment Probability using Panel Data

Figure 1. Office Buildings (1991)

Figure 2. Spatial Distribution of Offices for Lower Rents than Housing Rents in Tokyo’s 23
Special Wards in 1995 (Housing rent > Office rent)

21

Estimation of Redevelopment Probability using Panel Data

Figure 3. Spatial Distribution of Offices for Lower Rents than Housing Rents in Tokyo’s 23
Special Wards in 2000 (Housing rent > Office rent)

Figure 4. Spatial Distribution of Offices for Lower Rents than Housing Rents in Tokyo’s 23
Special Wards in 2004 (Housing rent > Office rent)22