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Office of Policy Development and Research (PD&R). Other services of HUD USER, PD&R’s 

Research and Information Service, include listservs; special interest, bimonthly publications 

(best practices, significant studies from other sources); access to public use databases; hotline 

1–800–245–2691 for help accessing the information you need.
Monitoring of Internal Moisture 

Loads in Residential Buildings

Prepared for:

U.S. Department of Housing and Urban Development

Washington, DC

Prepared by:

Lois Arena and Pallavi Mantha

Steven Winter Associates

Norwalk, CT

Achilles N. Karagiozis

Oak Ridge National Laboratory

Oak Ridge, TN

December 2010
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ii

Disclaimer

The statements and conclusions contained in this report are those of the authors and do not 

necessarily reflect the views or policies of the U.S. Department of Housing and Urban 

Development or the U.S. Government. The authors have made every effort to verify the 

accuracy and appropriateness of this report’s content. No guarantee of the accuracy or 

completeness of the information or acceptability for compliance with any industry standard or 

mandatory requirement of any code, law, or regulation is either offered or implied, however.

The products and systems described in the report are included only as examples of some 

available choices. No endorsement, recommendation, or evaluation of these products or their 

use is given or implied.
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iii

Acknowledgments

The U.S. Department of Housing and Urban Development, Office of Policy Development and 

Research, Affordable Housing Research and Technology Division funded this research. The 

authors especially appreciate Mike Blanford’s project leadership role and Peter Ashley’s 

support and advice from the Office of Healthy Homes and Lead Hazard Control. The authors 

also thank the members of ASHRAE SSPC 160 for their guidance throughout the project and 

particularly committee chair Anton TenWolde, who supported the project from initial concept.

Thanks also to all those who assisted with participant recruitment and the participants 

themselves who allowed researchers into their homes to conduct the survey.
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iv
FOREWORD 

Relative humidity is an important factor in the quality of the built environment. It affects thermal and 

respiratory comfort, occupant perceptions of indoor air quality (IAQ), and the energy necessary for 

heating and air conditioning. High relative humidity also favors growth of biological contaminants such as 

dust mites and mold. Elevated moisture levels over a prolonged period of time may also result in physical 

damage to the home. However, data related to moisture levels and coupled building features of 

residential building are lacking. HUD undertook this project primarily to create a data set that engineers 

and building scientists could use to better understand those relationships. This report describes the data 

and presents preliminary analysis. 

The study gathered a full year of indoor temperature and humidity data in a sample of sixty homes across 

three different climate regions - the hot/humid Southeast, the cold Northeast, and the marine Northwest. 

Multiple locations in each home were sampled every 15 minutes. This data set consists of over nine 

million individual temperature and humidity readings. To guide the study, HUD collaborated with 

members of the American Society of Heating, Refrigeration and Air-Conditioning Engineers, Inc. 

(ASH RAE) committee for the Criteria for Moisture-Control Design Analysis in Buildings. 

The data present information on the homes; including age, the air leakage rate, foundation type, heating 

and cooling equipment, and other observations from residents and evaluators. For example, while homes 

in the Northwest marine climates exhibited the highest average relative humidity levels (>50%), homes in 

the Northeast had the greatest occurrences of visible mold and musty smells. 

This report addresses issues of interest to HUD's Office of Healthy Homes and Lead Hazard Control 

(OHHLHC) and the Office of Policy Development and Research (PD&R). OHHLHC concentrates on 

identifying and mitigating hazards to resident health and safety, while PD&R develops improved methods 

for the design and construction of affordable, durable homes. The valuable data collected during this 

research advances the interests and goals of both offices. 

While this report provides a useful summary of the data, we expect the greatest value will occur when the 

data set is used to answer critical questions about ways to improve indoor air quality and building 

durability by designing homes to mediate indoor moisture levels. 

Pp 

Assistant 

Raphael 

Development 

W

Secretary 

. Bostic, 

and Research 

Ph

for 

.D

Policy 

. CLOffice 

Director 

of 

::Healthy ~ Homes and 

Lead Hazard Control 
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v

CONTENTS

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

Part 1. Research Design and Early Findings

Introduction ............................................................................................................. 3

Research Design..................................................................................................... 3

Review of Existing Research .................................................................................. 4

Climates Evaluated ................................................................................................ 6

Desired House Characteristics................................................................................ 7

Critical Parameters Measured and Recorded ........................................................ 7

Final Monitoring Protocol ....................................................................................... 9

Results and Discussion .......................................................................................... 11

Part 2. Research Findings in Three Different Climate Zones

Validation of Data Sets............................................................................................ 19

Overview of Results ................................................................................................ 20

Statistical Breakdown of Data ................................................................................. 22

Regional Analysis.................................................................................................... 28

Analysis of Homes With Moisture Problems ........................................................... 32

Problems Encountered and Lessons Learned ........................................................ 36

Conclusions............................................................................................................. 37

References.............................................................................................................. 40

Additional Reading .................................................................................................. 41

Appendix A. Prescreening Questionnaire ............................................................... 43

Appendix B. Field Data Collection Form ................................................................. 44

Appendix C. Monitoring Agreement ........................................................................ 47
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vi
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1

EXECUTIVE SUMMARY

An insufficient amount of measured data is available on actual indoor humidity levels in U.S. 

households, making it difficult to design durable homes. This research project has collected 1

full year of indoor temperature and humidity data for a sample of 60 homes across three 

different climate regions—the hot and humid Southeast (Zone 2), the cold Northeast (Zone 5),

and the marine Northwest (Zone 4).

This research was in direct support of the American Society of Heating, Refrigerating and AirConditioning Engineers (ASHRAE), Inc. Standard 160, Criteria for Moisture-Control Design 

Analysis in Buildings. A research methodology was developed with assistance from Oak Ridge 

National Laboratory (ORNL), a subcontractor and member of Standing Standards Project 

Committee 160 that acted in an advisory role. The monitoring protocol involved three site visits 

to each home to perform tasks such as collecting basic house and equipment characteristics, 

installing data loggers, performing testing to quantify envelope leakage and duct leakage, and 

collecting data recorded by the loggers. Data compiled in the field tests were analyzed to 

identify the potential relationships between certain household characteristics and the 

measured internal humidity levels.

This report presents significant findings from the study. Correlations between indoor moisture 

levels and climate, occupant density, and house characteristics are the focus of the results.

Conclusions and recommendations for indoor moisture management or future research needs 

are also discussed.

Of the three climates in the study, the homes in the marine climate appear to consistently see 

indoor relative humidity levels above 50 percent, but the highest level of moisture problems 

occurred in climate Zone 5, the cold climate. 

After the initial review of the data, it appears that major differences between the housing sets 

include the following:

• Age. Homes in the marine climate were much older than in the other two climates.

• Air leakage. The air-leakage rate (ACH50) was almost twice as high in the marine 

climate as in the other two climates.

• Foundation type. Several homes in the marine climate were built on vented 

crawlspaces with dirt floors. The homes in the hot, humid climate were all built on slabs 

and the homes in the cold climate were primarily built on partially finished basements 

that were conditioned.

• Cooling equipment. Only 20 percent of the homes in the marine climate had central 

air-conditioning units, whereas 75 percent of the homes in the cold climate and 100

percent of the homes in the hot and humid climate had central air-conditioning.

• Heating equipment. Only about 50 percent of the homes in the marine climate had 

forced-air heating. The other 50 percent had a mixture of boilers with baseboard 

radiators or electric heat.
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2

In all three regions, the highest occurrence of visible mold or moisture damage was on or 

around the windows and in the bathrooms. In the hot-humid climate, mold was visible on 

several air handlers around the cooling coil, usually on air handlers located outside the 

conditioned space, such as in a garage.

In the cold climate, moisture problems included musty smells within the conditioned space that 

were reported by the occupants and confirmed on site. Several homes had water leakage 

problems in the basements. In a couple of instances these smells were noted on the upper 

floors of the home as opposed to the basements.

Strong correlations between house characteristics and indoor humidity levels were not 

possible due to the small sample size. After evaluating the data regionally and with respect to 

those homes that did and did not have moisture problems, trends that deserve further 

investigation are increased humidity levels due to—

• High air-change rates. • High occupant densities.

• The presence of unfinished and unconditioned basements and crawlspaces. • The use of materials with higher condensation and mold potential in climate Zone 2, hot

and humid climate, such as metal windows, marble window sills, and so on.
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3

PART 1. RESEARCH DESIGN AND EARLY FINDINGS

Introduction

A complete understanding of the influences certain factors have on a home’s overall moisture 

content and moisture performance is not available. Which is more harmful to a home: 

showering without the fan on or having inefficient single-pane or metal windows? The U.S. 

Department of Housing and Urban Development (HUD) funded a research project that 

measured interior relative humidity. The purpose of the research was to identify and quantify 

moisture loads on a home, and it targeted three different regions in the United States—the hot

and humid Southeast (Zone 2), the cold Northeast (Zone 5), and the marine Northwest (Zone 

4). During an initial site visit, an engineer collected house and household characteristic data, 

including occupancy levels, insulation levels, equipment efficiencies, envelope leakage, and 

duct leakage. This information will aid researchers and engineers in developing construction 

standards and best practice guidance that will reduce the likelihood of new homes having 

moisture-related problems.

After obtaining yearlong exterior and interior moisture load data for the test homes, engineers

conducted an analysis of the influence of various components of the home and occupantrelated activity. This analytical activity should provide all the data required for the analysis.

Engineers analyzed data compiled in the field tests with the intention of identifying 

relationships between the various household characteristics and the internal humidity levels.

Research Design

The following section introduces the objectives and goals of this study. The development of the 

monitoring protocol is also described in detail.

Objectives

Engineers focused on three major objectives for conducting this study.

1. Research Support. As noted in a Buildings VIII paper (TenWolde and Walker, 2001: 1),

“computer models are increasingly used to make recommendations for building design in 

various climates. However, results obtained with these models are extremely sensitive to the 

assumed moisture boundary conditions.” One intention of this HUD project was to provide the 

research community with critically important field data for defining boundary conditions for use 

in moisture models and, through that effort, help that community better understand the effect of 

moisture on the durability of homes.

2. Support for Development of Design Criteria. The American Society of Heating, 

Refrigerating and Air-Conditioning Engineers (ASHRAE), Inc., Standing Standard Project 

Committee 160 continues to maintain the relatively new standard, “Criteria for Moisture-Control 

Design Analysis in Buildings.” This committee has formulated “performance design criteria for 
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4

predicting, mitigating, or reducing moisture damage to the building envelope, materials, 

components, systems and furnishings” (ASHRAE, 2009: 4). This moisture design standard will 

help make homes more moisture resistant and thus more durable. Data collected during this 

project provides documented support for the interior design loads adopted by the committee 

with the hope that the resulting design criteria will minimize durability problems associated with 

high moisture levels.

3. Identify Influences on the Moisture Levels in Homes. Residential interior moisture loads 

are influenced by a multitude of variables, including the following:

• Climate.

• Construction materials.

• Building envelope tightness.

• Type, size, and control of mechanical equipment.

• Size and configuration of the home.

• Number of occupants and their behaviors.

• Moisture capacitance of furnishings.

• Age of home.

Although the data set collected during this study is somewhat limited, it was intended that the 

proposed project analyses would identify correlations between interior and exterior conditions 

and moisture levels in typical single-family detached homes.

Goals

This project attempted to address a combination of two recommended research projects that 

each received a “very high” priority ranking in the HUD publication, Building Moisture and 

Durability: Past, Present, and Future Work (Dacquisto, Crandell, and Lyons, 2004):

• Characterize the moisture performance of existing homes through a monitoring protocol.

• Develop statistically validated procedures to assess internal moisture loads for use in 

hygrothermal analyses and related engineering studies.

The scope of the proposed research project was not sufficient to monitor several hundred

homes around the country, however, and statistical validation is unlikely. What this project has 

provided is a sound monitoring protocol and start at developing a critically important database

of information for moisture modeling and standards development.

Review of Existing Research

The monitoring protocol was developed with the help of an advisory panel composed of 

experts from different segments of the building industry, most of whom are members of the 

ASHRAE Standing Standard Project Committee (SSPC) 160.

In addition to receiving the input from the SSPC 160 committee, this study was supported by 

Oak Ridge National Laboratory (ORNL), which has been directly funded by the U.S. 
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5

Department of Energy to support the ASHRAE Standard 160 process. In an effort to determine 

what information was most critical, subcontractor ORNL reviewed copies of 10 hygrothermal 

models. During the past two decades, a number of computer simulation tools have been 

developed to predict thermal and moisture conditions in buildings and the building envelope. In 

addition to using these forensic tools to investigate building failures, engineers increasingly use 

these computer models to make recommendations for building design in various climates.

The SSPC 160 committee realized that requiring the use of multidimensional models was 

inconsistent with its goal of creating a standard that could be easily used by the design 

community. In Section 5 of the standard, however, the committee listed a series of criteria that 

any computer tool needed to satisfy.

For those models that met the requirements, engineers examined the input variables and data 

format requirements to ensure that the data generated by this HUD project would be 

compatible and useful to each of these simulation models.

Results obtained with this type of model are extremely sensitive to the assumed moisture 

boundary conditions. For instance, during winter in cold climates, the moisture conditions in 

walls depend greatly on the indoor humidity conditions. Moisture capacitive walls, such as 

brick-clad walls, will have their performance vary greatly based on the quantity of wind-driven 

rain. The SSPC 160 committee correctly realized that a consistent approach to moisture 

design demands a consistent framework for design assumptions or assumed loads.

The ASHRAE Standard 160 describes three options for estimating the interior conditions.

These options contain varying amounts of input data to calculate; however, the options are 

missing a database of typical temperature and humidity loads that the user of the standard can 

apply to compare with his or her estimations. Although great strides have been made to 

quantify and standardize meteorological data, such as wind-driven rain, insufficient data exist 

on typical indoor conditions. The primary purpose of this HUD project was to generate some of 

these data.

Engineers conducted a review of data from other research studies similar in nature to this one 

to aid in the study design; address unanswered questions, if possible; and to potentially 

supplement the data collected during this HUD study. Although these data sets proved useful 

in determining characteristics that should be recorded, climate zones on which to focus, and 

desired length of the collection period, they could not be used to supplement the data set from 

this study. Engineers determined during this review that vital information was missing from 

each study in one form or another. For instance, several studies produced numerous data 

points on relative humidity and temperature but had not collected detailed information about 

the house characteristics (Piggs, 2003), had been conducted only in one climate (Aoki-Kramer

and Karagiozis, 2004; Kalamees, 2006), had collected data for only one month (Kumaran and 

Sanders, 2008), and so on.
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6

Climates Evaluated

In this HUD study, engineers emphasized three climate regions of the country that are the 

focus of moisture and related durability studies. The plan as proposed was to have a greater 

sample of homes for a smaller sample of climates. It was hoped that the greater sample size 

would better characterize the variability within a climate region and enable the researchers to 

develop maximum, minimum, and average profiles for modeling and design studies. The three 

important climate regions are, for different reasons, the marine Northwest, the cold Northeast, 

and the hot and humid Southeast. (See figure 1.)

Zone 4, the marine Northwest, is an area of high to extreme rainfall amounts. It is also an area 

of rather moderate temperatures, minimizing the potential drying influence of heating or airconditioning system operation. Building envelope failures in this region are known, and 

numerous moisture design studies have been performed with internal load assumptions, based 

on very limited data. 

Figure 1. IECC Climate Zone Map of the United States

For residences in Zone 5, the cold Northeast, the internal moisture load assumptions are 

extremely important, because the primary cause of durability problems is moisture-laden 

internal air entering into the envelope system and the subsequent condensation of the 

moisture on cold surfaces. TenWolde (2000) documents this problem well and describes how 

design criteria, such as that developed by ASHRAE Standard 160, would have alerted builders 

to the potential problem.
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7

Moisture issues in Zone 2, the hot-humid Southeast, are more influenced by ambient humidity 

levels, but the extent to which the exterior humidity influences interior humidity levels is not 

well understood. Factors such as envelope tightness, the presence and operation of a 

mechanical ventilation system, and the dehumidification performance of the home’s airconditioning system can significantly affect the indoor humidity conditions. Rudd and 

Henderson have conducted relevant monitoring studies and research in this climate region 

(Rudd and Henderson, 2007). Engineers thoroughly reviewed Rudd and Henderson’s 

research, which served as guidance for the monitoring protocol.

Desired House Characteristics

After evaluating existing research and discussing goals with the SSPC 160 committee, 

engineers decided that homes with the following characteristics would provide the most useful 

data sets:

• Single-family homes (preferably detached).

• More than 1 year old.

• Less than 3,000 square feet.

• At least two occupants (preferably more) with no plans to move within the next year. 

• No major renovation or remodeling work planned within the next year.

• Engineers wanted a range of characteristics and occupant densities.

Identifying and selecting test homes for this project was a critically important task. Simply put, 

without the homes, the study would have no data. Researchers were careful to ensure that the 

recruitment process avoided selective biases that might occur. For instance, occupants who 

were having problems or concerns about moisture and humidity problems in their homes may 

have been more inclined to participate.

Engineers found test homes through the following sources:

• Building America builder partners.

• Local agencies and institutes, such the Florida Energy Extension Service.

• Steven Winter Associates (SWA) employees’ relatives and friends.

• Study participants.

Critical Parameters Measured and Recorded

In addition to determining the number of climate zones and the types of homes to be 

monitored, researchers used two key elements to the monitoring protocol:

• The test home characterization (short-term data collection).

• The internal moisture load monitoring (long-term data collection).
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8

Short-Term Data Collection

As noted previously, the internal moisture load can be dependent on a multitude of home 

characteristics. An assessment of these characteristics for each test home would be important 

to subsequent analyses to help understand variability and key relationships. One critical task 

during the development of the monitoring protocol was to determine which house 

characteristics were vital to assessing internal moisture loads. The result of that analysis was 

translated to the Field Data Collection Form (see appendix B). Engineers developed this form 

to ensure consistency and completeness in the data collection process. They completed the 

form for each home during the initial site visit.

Short-term testing and data collection, which engineers conducted at the time of monitoring 

equipment installation, included the following:

• A blower door test to quantify envelope tightness.

• A test to quantify duct leakage to the exterior.

• A description of the envelope detail, including insulation type and quantity, siding 

materials, flooring materials, and so on.

• A description of the heating, ventilation, and air-conditioning equipment, including the 

type; capacity; and presence and description of humidifiers, dehumidifiers, and 

mechanical ventilation systems.

• Documentation of the house size and configuration and the number of occupants.

• Measurement of exhaust fan airflows.

• Presence of mold and moisture sources.

Researchers transferred all results collected during the evaluation of the home characteristics 

into a database for subsequent analysis.

Long-Term Data Collection

When evaluating the tools available for the long-term monitoring, researchers considered the 

following issues:

• Available memory.

• Logging frequency.

• Durability.

• Accuracy.

• Intrusiveness.

• Cost.

Engineers conducted preliminary research on wireless loggers, but 

they found that the wireless versions were too expensive for this 

HUD project. They found that the independent (HOBO) data loggers

from Onset Computer had a lower cost, were nonintrusive, and 

were relatively simple to use; so they selected the HOBO.
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9

Subsequent to the short-term assessment, engineers installed the HOBOs for long-term 

monitoring of temperature and relative humidity. The data loggers collected the following data:

• Outdoor temperature and relative humidity.

• Primary living space (family and great room) temperature and relative humidity.

• Master bedroom temperature and relative humidity (to observe diurnal variations, which

could be significant and of interest).

• Primary bathroom (where most showers were taken) temperature and relative humidity 

(could represent a severe humidity-load condition that could influence the entire home).

• Basement or crawlspace temperature and relative humidity (if present, could be a high 

moisture-load region of the home).

• Attic temperature and relative humidity where a slab foundation was present (could 

represent significant diurnal moisture loading).

Engineers set up each data logger to record temperature and relative humidity data every 15 

minutes during a 12-month period. (These data have been averaged during post-processing to 

provide hourly data for model input.)

Final Monitoring Protocol

Using information obtained during the review of existing relevant research projects and 

moisture-prediction models, and in combination with the goals of the SSPC 160 committee, 

engineers developed the final monitoring protocol. The following items are the major 

components of the final monitoring protocol.

Prescreening Telephone Call

Engineers informed the potential participants of the requirements of the study when they asked 

them to participate, unless the participants were a last minute addition to the study. The 

engineers used a prescreening questionnaire in a telephone call to confirm that the home was,

in fact, a good candidate and to ensure that the occupants understood the reason for the 

study, what was involved if they participated, and the length of time their home would be 

monitored. (See the prescreening questionnaire in appendix A.)

Site Visits

To gather the data, the engineers made three site visits to each home. In addition to making 

the initial visit to collect house characteristics on the Field Data Collection Form and install the 

HOBOs and the final visit to collect the data loggers, the engineers made an interim site visit 

because the data loggers were capable of storing only about 200 days worth of data at 15-

minute intervals.

Before scheduling the initial field visits, the engineers conducted a trial run on one home in the 

cold Northeast. They conducted a trial run for several reasons. First, they wanted to confirm 

the time needed to gather information during the first site visit so that they could efficiently 
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10

schedule appointments. Also, they needed to work out the best locations for the HOBOs and 

the best methods for installing them. The engineers had two concerns at this point. First, they 

wanted to keep the data loggers out of the reach of children and in locations where they would

be least likely to get moved or harmed. Second, they wanted to make sure that the HOBOs 

were attached in a manner that could not damage the participants’ property.

A second major reason for the trial run was that the procedure for measuring duct leakage, 

known as the DeltaQ method, was relatively new and uncommon. This method entails using 

the blower door to pressurize and depressurize the home with the air-handler fan running and 

with it off. Using a software program developed by the Energy Conservatory and Lawrence

Berkeley National Laboratory (LBNL), the pressures recorded during those tests are analyzed 

and the duct leakage to the exterior is calculated. This method was selected to minimize the 

time needed to conduct the initial site visit. Conducting duct blaster tests on existing homes 

can take a significant amount of time if furnishings need to be moved, and it increases the 

possibility of damage to the occupants’ personal property. During this trial run, engineers

worked out problems and questions about the proper procedure for conducting a DeltaQ test.

Third, and finally, the test run could determine if the field checklist needed any last minute 

additions or deletions before printing them in bulk.

The final preparation for the initial visit included calibrating the sensors. Each sensor was 

started and allowed to log data for a minimum of 24 hours. The engineers then analyzed the 

results for accuracy, according to the manufacturer’s specifications. The test showed that all 

data loggers appeared to be operating within the tolerances allowed.

Initial Site Visit (about 2 to 3 hours). During the initial visit, the engineers recorded the basic 

house and equipment characteristics, made air-leakage and duct-leakage measurements, and 

installed the data loggers (three inside, one outside, and one in the attic, basement, or

crawlspace). They also conducted an interview with the occupant to confirm occupancy 

schedules, comfort problems, and any upgrades they may have made. (See appendix B for a

more detailed explanation of the information collected.)

Interim Site Visit (1 hour). The engineers collected the first 6 months of data recorded during 

the interim site visit. They also used this visit was to check on the condition and location of the 

data loggers and to talk with the occupants about any changes that may have occurred during

the past 6 months. In addition, they collected any house information that had been missed 

during the initial visit.

Final Site Visit (1 hour). The final site visit was much the same as the interim visit, with the 

additional task of removing the data loggers.
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Participation Agreement and Compensation

The engineers executed a written agreement with the test home occupants. This agreement 

established the expectations and responsibilities of all parties involved. This written agreement 

achieved a secure commitment of cooperation by the occupants. In exchange for volunteering 

to participate in the study, the occupants received an audit that explained the efficiency levels 

found in their home and compared those values with the study averages for their region and

with the ENERGY STAR values for a new home built in their area. Basic recommendations for 

moisture-barrier improvements were made if applicable. (See this agreement in appendix C.)

Results and Discussion

This study generated four data sets of internal temperature and relative humidity for each 

home. Thus, for each climate region with 20 homes, the engineers had 80 data sets for 

analyzing interior relative humidity and temperature. In addition, they had 15 to 20 data sets 

per region from exterior sensors that had measured temperature and relative humidity.

Engineers analyzed the data to identify the potential relationships between certain household 

characteristic data and the measured internal humidity levels.

They performed an evaluation of the data to ensure that all data were collected for each 

sensor and that the data appeared to be valid. This review indicates that less than 2 percent of 

data were lost overall; 1.3 percent of the total lost data was in the marine climate. Of the 285 

data loggers installed, only 1 was not retrieved, and approximately 10 different data loggers

stopped collecting at some point during one of the 6-month periods, between visits. Only two 

data loggers were determined to have obviously bad data.

The engineers first analyzed the data by region. They calculated averages of interior relative 

humidity and temperature and compared them with the average outdoor values collected 

during the same period. Because relative humidity is a function of temperature, they converted 

this value to humidity ratio (lbw/lbda) to determine the actual amount of water in the air. Tables 1 

through 3 show the average interior values from the collected data for each region compared 

with the ambient conditions.
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12

Table 1. Monthly Averages of Interior and Exterior Temperature and Relative Humidity 

Data: Zone 2, Hot-Humid Southeast

Zone 2 Indoor Outdoor

Month

Temperature

[°F]

Humidity

Ratio

[lbw/lbda]

Relative

Humidity

Temperature

[°F]

Humidity

Ratio

[lbw/lbda]

Relative

Humidity

January 72.8 0.00907 52.58 59.0 0.00766 69.02

February 72.3 0.00837 49.32 60.1 0.00730 64.42

March 74.5 0.00945 51.71 67.4 0.00958 66.88

April 75.8 0.01002 52.45 71.9 0.01075 65.51

May 77.8 0.00976 47.79 79.2 0.01324 64.04

June 78.4 0.01004 47.92 81.5 0.01631 72.58

July 78.1 0.01013 48.82 81.5 0.01743 76.54

August 77.9 0.01044 50.72 81.9 0.01790 77.54

September 77.9 0.01026 49.90 81.2 0.01691 74.69

October 76.4 0.00999 50.99 73.4 0.01278 71.08

November 73.8 0.00951 53.13 63.3 0.00913 70.88

December 73.8 0.01018 56.92 64.1 0.00962 73.58

Annual 75.80 0.00977 51.02 72.03 0.01238 70.56

Table 2. Monthly Averages of Interior and Exterior Temperature and Relative Humidity 

Data: Zone 5, Cold Northeast

Zone 5 Indoor Outdoor

Month

Temperature

[°F]

Humidity 

Ratio

[lbw/lbda]

Relative 

Humidit

y

Temperature

[°F]

Humidity

Ratio

[lbw/lbda]

Relative 

Humidity

January 64.8 0.00475 36.1 20.9 0.00188 74.10

February 65.5 0.00494 36.7 28.6 0.00258 71.63

March 65.7 0.00522 38.3 37.0 0.00315 63.17

April 67.8 0.00635 42.9 49.7 0.00466 60.65

May 70.0 0.00766 48.2 59.5 0.00689 62.83

June 73.9 0.00951 52.1 68.3 0.01023 67.76

July 75.8 0.01087 56.1 73.0 0.01240 71.22

August 74.1 0.01057 57.7 68.6 0.01100 73.63

September 72.0 0.00988 57.9 64.1 0.00981 75.20

October 67.0 0.00803 56.2 49.5 0.00570 73.75

November 65.9 0.00683 49.8 39.7 0.00433 77.53

December 65.3 0.00558 41.8 30.2 0.00284 74.67

Annual 68.98 0.00752 47.81 49.09 0.00629 70.51
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13

Table 3. Monthly Averages of Interior and Exterior Temperature and Relative Humidity 

Data: Zone 4, Marine Northwest

Zone 4 Indoor Outdoor

Month Temperature

[°F]

Humidity 

Ratio

[lbw/lbda]

Relative 

Humidity

Temperature

[°F]

Humidity 

Ratio

[lbw/lbda]

Relative 

Humidity

January 63.50 0.00622 50.08 39.50 0.00445 84.63

February 63.70 0.00609 48.70 41.76 0.00436 78.30

March 64.30 0.00634 49.53 44.05 0.00460 75.10

April 66.00 0.00688 50.50 51.41 0.00526 67.55

May 68.45 0.00769 51.93 58.76 0.00648 63.97

June 71.06 0.00880 54.11 64.67 0.00815 64.13

July 72.28 0.00864 51.10 66.89 0.00815 60.08

August 73.21 0.00960 54.76 67.23 0.00938 67.45

September 70.21 0.00862 54.78 62.49 0.00794 68.31

October 65.67 0.00795 58.81 52.53 0.00660 77.99

November 64.37 0.00784 60.62 49.09 0.00641 85.21

December 62.95 0.00627 51.32 37.67 0.00431 85.52

Annual 67.14 0.00758 53.02 53.00 0.00634 73.19

Figures 2 through 4 are graphical representations of the temperature and relative humidity 

data presented in tables 1 through 3.
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14

Figure 2. Monthly Averages of Interior and Exterior Temperature and Relative Humidity 

Values for Zone 2, Hot- Humid Southeast, From Study Data

Figure 3. Monthly Averages of Interior and Exterior Temperature and Relative Humidity 

Values for Zone 5, Cold Northeast, From Study Data

0.0 

10.0 

20.0 

30.0 

40.0 

50.0 

60.0 

70.0 

80.0 

90.0 

100.0 

Jan  Feb  Mar  Apr  May  Jun  Jul  Aug  Sep  Oct  Nov  Dec 

Temperature (F)/Rela0ve Humidity 

Zone 2, Monthly Averages from Collected Data 

Temp. ‐ Int.  Rel. Hum.  ‐ Int.  Temp. ‐ Ext.  Rel. Hum. ‐ Ext. 

0.0 

10.0 

20.0 

30.0 

40.0 

50.0 

60.0 

70.0 

80.0 

90.0 

100.0 

Jan  Feb  Mar  Apr  May  Jun  Jul  Aug  Sep  Oct  Nov  Dec 

Temperature (F)/Rela0ve Humidity 

Zone 5, Monthly Averages from Collected Data 

Temp. ‐ Int.  Rel. Hum.  ‐ Int.  Temp. ‐ Ext.  Rel. Hum. ‐ Ext. 
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15

Figure 4. Monthly Averages of Interior and Exterior Temperature and Relative Humidity 

Values for Zone 4 From Study Data

Next, the engineers compared the average building component characteristics in each region 

with each individual participant’s values. They used these comparisons in audit reports sent to 

each participant in exchange for their agreement to participate in this study. Table 4 shows the 

average building age, size, foundation type, and building component efficiencies for each of 

the three regions.

0.00 

10.00 

20.00 

30.00 

40.00 

50.00 

60.00 

70.00 

80.00 

90.00 

100.00 

Jan  Feb  Mar  Apr  May  Jun  Jul  Aug  Sep  Oct  Nov  Dec 

Temperature (F)/Rela0ve Humidity 

Zone 4, Monthly Averages from Collected Data 

Temp. ‐ Int.  Rel. Hum.  ‐ Int.  Temp. ‐ Ext.  Rel. Hum. ‐ Ext. 
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16

Table 4. Average House Characteristics by Region

Component

Zone 2, HotHumid

Southeast

Zone 5, Cold

Northeast

Zone 4, Marine

Northwest

Year built 1998 1966 1947

Size (conditioned square feet) 1,989 3,118 2,059

# of occupants 3.45 3.10 3.1

Air leakage (ACH50) 6.0 6.1 11.1

Attic R-value (hft2°F/Btu) 22 36 24

Wall R-value (hft2°F/Btu) 12 12 7

Dominant foundation type Slab Partially finished 

basement

Basement and 

crawlspace

Duct leakage (cfm/100ft2 of conditioned space) 5.6 4.7 13.9

Dominant heating type AS heat pump Furnace Furnace

Cooling efficiency (SEER) 11.54 10.22 14.00

Dominant domestic hot water fuel Electric Gas Gas

Homes with mechanical ventilation (%) 0% 25% 30%

Homes with cooling (%) 100% 75% 20%

Average interior relative humidity (%) 51.7% 47.9% 53.1%

Homes with moisture problems (%) 35% 50% 35%

Note: ACH50 Hourly air change rate at a pressure difference of 50 Pascals between inside and outside.

Table 4 shows that the highest occurrences of moisture problems were noted in homes in 

Zone 5, the cold Northeast data set. In this report, “moisture problems” refer to any mold or 

moisture damage or intrusion that engineers noted during the initial site inspection. These 

moisture problems were a combination of moisture in the basements (which was typically in 

the spring or fall), mold on the window casings, and musty smells. On average, indoor relative 

humidity values are highest in the marine climate, with average monthly values above 50

percent during most of the year. The marine climate had the fewest homes with central airconditioning and the most homes with crawlspaces.

Table 5 summarizes the house characteristics for the homes with moisture problems 

compared with those homes without moisture problems for each region. What is interesting 

when looking at this table is that the trends that seem plausible in the cold and marine 

climates, do not apply to the hot and humid climate. For instance, there seems to be a distinct 

difference in air changes at 50 Pascals (ACH50) for homes with and without moisture 

problems in the marine and cold climates, but not in the hot and humid zone. This table implies 

that blanket recommendations for humidity control cannot be made based on most of the 

characteristics evaluated during this study, at least not without further research. Too many 

variables are in play to draw significant conclusions from these data. Each climate has specific 
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17

characteristics, which are influencing interior moisture levels. More research is needed to 

determine if the trends suggested in this research are statistically significant.

Table 5. Summary of House Characteristics for Homes With Moisture Problems Versus

Homes Without Moisture Problems for Each Climate Zone Region

Zone 2, Hot-Humid

Southeast Zone 5, Cold Northeast Zone 4, Marine Northwest

House Characteristic

Moisture 

Problems

No

Moisture 

Problems

Moisture 

Problems

No 

Moisture 

Problems

Moisture 

Problems

No 

Moisture 

Problems

House size (conditioned

square feet) 1,860 2,059 2,819 3,416 1,701 2,251

Year built 2002 1995 1956 1976 1939 1952

Interior temperature (°F) 75.7 75.8 68.2 69.8 67.2 67.1

Dehumidifier (% of homes) 0 0 80 90 0.00 31

Primary foundation Slab Slab

Partially 

finished 

basement

Partially 

finished 

basement

Crawlspace and 

partial 

crawlspace

Mixed

Air leakage (ACH@50) 5.29 6.38 7.50 4.80 15.4 7.7

Occupant density (#/ft2) 0.00194 0.00175 0.00103 0.00105 0.00227 0.00144

Bath fans (% of homes) 86% 92% 90% 100% 71% 69%

Mechanical ventilation (% of 

homes) 0.00 0.00 10% 20% 0% 23%

Humidity ratio (lbw/lbda) 0.00971 0.00986 0.00755 0.00751 0.00767 0.00755

A general analysis of the tables and graphs in this section, combined with information gained 

during the site inspections, suggests the following:

Zone 2, Hot-Humid Southeast. The newer homes in this data set have more moisture 

problems than the older homes do, although the humidity ratios are not significantly different.

The data may suggest a correlation with more efficient, newer homes and the lack of 

conditioning needed at times than the less efficient older homes would need, or they could 

suggest a construction failure particular to this community. Several of the newer homes were in 

the same development. Builders should avoid installing metal windows and other exposed cold 

surfaces in homes in this region.

Zone 5, Cold Northeast. Water seepage into the foundation appears to be a common 

problem in this data set. Engineers found mold only on the inefficient windows in this group—

those having single-pane windows or single-pane windows with storm windows. Air-change 

rate would appear to be one factor affecting homes with moisture problems in this region.

Another factor could be an association with older homes without good foundation moisture 

control.
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Zone 4, Marine Northwest. The homes with moisture problems in this region all had at least a 

partial crawlspace foundation with exposed dirt floors. Some of these crawlspaces were vented 

and some were not, but none had a well-sealed vapor barrier. As in the homes in Zone 5, the 

air-change rate would appear to be one factor affecting homes with moisture problems, or it 

could be an association with older homes that coincidentally lack good foundation moisture

control. More research is necessary to determine if either of these factors significantly 

influences interior moisture levels.
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PART 2. RESEARCH FINDINGS IN THREE DIFFERENT CLIMATE ZONES 

Validation of Data Sets

To identify extreme outliers and potentially bad sensors, the engineers graphed each sensor’s 

raw data against the other sensors in that region for the same location in the home. For 

example, figure 5 shows the data collected for all the ambient sensors in Florida.

Figure 5. Temperature and Relative Humidity Data From Zone 2, Hot-Humid Southeast, 

Climate Ambient Sensors

As can be seen in the relative humidity portion of the graph, one sensor recorded values 

significantly lower than the rest in December. After some investigation, the engineers 

determined that this sensor simply stopped recording data for a period of 3 days.

The engineers analyzed each set of sensors in the same manner. They determined some 

other reasons for data anomalies include the following:

• Placing sensors too near a heating or cooling source, such as a leaky duct or fireplace.

• Occupants with unusual thermostat set points. • Extended periods when homes were unoccupied. • Upgrades to homes—a couple of occupants changed windows.

• Unexplained behavior—in one Florida home, all interior temperature sensors read 55 °F

in August.
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In general, review of the data confirmed that minimal losses of data or bad data occurred 

during the 12-month collection period. More than 97 percent of the data were successfully 

collected. Data were not eliminated due to unusual situations such as those listed in the 

previous list. Data were excluded only if the sensors were thought to be bad—that is, single 

digits were recorded on only one of the four sensors on the inside of the home, negative 

numbers were logged, and so on. As explained previously, the engineers calibrated all sensors 

before installation.

Overview of Results

These data are summarized in the box-and-whisker diagrams in figures 6 through 9. Mean

values along with minimum, maximum, and median humidity values are listed below each plot.

All whiskers represent 1.5 times the interquartile range, or 1.5 times the upper 75th percentile 

(indicated by the upper edge of the light green box) minus the lower 25th percentile (indicated 

by the lower edge of the dark green box). The percent outliers describe the percentage of the 

data collected that lies outside the whiskers. The circles represent the minimum and maximum

outliers. All box plots are based on 15-minute-interval data collected during an entire year.

Figure 6. Box Plot for Indoor Humidity Ratio for All Three Climate Zones
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Figure 7. Box Plot of Indoor Relative Humidity for All Three Climate Zones

Figure 8. Box Plot of Outdoor Humidity Ratio for All Three Climate Zones
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Figure 9. Box Plot of Outdoor Relative Humidity for All Three Climate Zones

Statistical Breakdown of Data

Figures 10 through 15 show box plots of the average humidity ratios and relative humidity for 

each sensor location, for each region. For each of the three climate zones, the humidity ratios 

and relative humidity are quite uniform for all the interior sensors, with the bathroom sensor 

consistently showing slightly higher values.
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Figure 10. Humidity Ratio Box Plots for Zone 2, Hot-Humid Southeast Climate for Each 

Sensor Location
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Figure 11. Relative Humidity Box Plots for Zone 2, Hot-Humid Southeast Climate for 

Each Sensor Location
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Figure 12. Humidity Ratio Box Plots for Zone 4, Marine Northwest Climate for Each 

Sensor Location

0.000 

0.005 

0.010 

0.015 

0.020 

0.025 

Living Room Bedroom Bathroom Foundation Outdoor 

Humidity Ratio 

Zone 4 
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Figure 13. Relative Humidity Box Plots for Zone 4, Marine Northwest Climate for Each 

Sensor Location

0.000 

20.000 

40.000 

60.000 

80.000 

100.000 

120.000 

Living Room Bedroom Bathroom Foundation Outdoor 

Relative Humidity 

Zone 4 
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Figure 14. Humidity Ratio Box Plots for Zone 5, Cold Northeast Climate for Each Sensor 

Location

0.000 

0.005 

0.010 

0.015 

0.020 

0.025 

0.030 

Living Room Bedroom Bathroom Foundation Outdoor 

Humidity Ratio 

Zone 5 
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Figure 15. Relative Humidity Box Plots for Zone 5, Cold Northeast Climate for Each 

Sensor Location

Regional Analysis

As noted previously in Part 1, Results and Discussion, the highest occurrences of moisture 

problems were noted in the Zone 5, cold Northeast housing data set, and the highest interior 

relative humidity of the three zones was in the Zone 5, marine Northwest housing data set, 

which also had a higher humidity ratio than housing in the cold Northeast data set. Several of 

the moisture problems in the cold Northeast were associated with water leakage into the 

basement. 

The marine Northwest data set had the fewest homes with central air-conditioning and the 

most homes with crawlspaces. These basic results suggest possible correlations among 

indoor relative humidity and cooling-system use and operation, heating-system use and type, 

foundation type, and climate.

Although humidity ratios are not that different for the cold and marine climates, the marine 

climate is milder, as figure 16 shows. These milder conditions result in less of a need for space 

conditioning and, thus, dehumidification.

0.000 

20.000 

40.000 

60.000 

80.000 

100.000 

120.000 

Living Room Bedroom Bathroom Foundation Outdoor 

Relative Humidity 

Zone 5 
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29

Figure 16. Monthly Outdoor Temperatures for Each Region

A vapor-pressure analysis of the monitored homes revealed that the average annual interior 

vapor pressure was approximately 1,550 Pascals for Zone 2, 1,213 Pascals for Zone 4, and 

1,192 Pascals for Zone 5. Looking at the monthly differences between interior and exterior 

vapor pressure as shown in figure 17 gives a good indication of the direction of the flow of 

moisture in each climate. The very large negative difference between interior and exterior 

vapor pressure indicates that the flow of moisture is likely to be from outside to inside for at 

least 6 months of the year in Zone 2. The results for the cold climate suggest a change in the 

direction of the moisture flow, but the primary direction appears to be from inside to outside 

during at least 6 to 7 months of the year. Zone 4 shows a consistent positive vapor pressure 

difference for the entire year.
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Figure 17. Difference Between Interior and Exterior Vapor Pressure (Pascals) for the 

Three Climate Zones

Correlations between house characteristics and internal moisture loads were investigated 

within each region for—

• Number of occupants.

• Occupant density.

• House size.

• Foundation type.

• Air leakage.

• Mechanical ventilation.

The most consistent trends appear to be between indoor humidity and (1) occupant density, 

although even that is questionable with coefficients of determination (R2

) not much higher than 

0.2 (see figures 18 and 19), and (2) the presence of a foundation with exposed dirt floors.

Further research is needed to determine if significant correlations exist between these two 

housing characteristics.

‐1500 

‐1000 

‐500 

0 

500 

1000 

Vapor Pressure Difference (Pa) 

Interior and Exterior Vapor Pressure (Pa) 

marine  cold  hot humid 
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Figure 18. Humidity Ratio versus Occupant Density for all Climate Zones

Figure 19. Humidity Ratio versus Occupant Density for Each Climate Zone
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Analysis of Homes With Moisture Problems

The engineers conducted all site inspections between late May and mid-July. They questioned 

all occupants about the presence of mold or moisture problems in their homes during the 

course of the entire year and verified all reported concerns during the site visits.

In all three regions, the highest occurrence of mold or moisture damage observed during the 

initial site visits was on or around the windows and in the bathrooms. In the hot and humid 

climate, mold was visible on several air handlers around the cooling coil, usually on air 

handlers located outside the conditioned space, such as in a garage. Also, several incidents of 

moldy caulk were found on the new homes in Gainesville, FL. This issue was specific to this 

housing development, which may mean that the caulk used during construction was not mold 

resistant and is not an indication of a typical problem in this region.

In the cold climate, moisture problems included musty smells within the conditioned space that 

were reported by the occupants and confirmed on site. Several homes had moisture leaks in 

the basements for part of the year. In more than one instance musty smells were noted on the 

upper floors of the home as opposed to the basements. Table 6 summarizes the moisture 

problems noted during the site inspections.
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Table 6. Summary of Moisture Problems in Study Homes

House 

#

Moisture Problem Potential Moisture Source

Climate: Hot-Humid

4 Mold on ceiling and windows in baths and on air handler in garage No bath fans

6 Mold on windows on 2nd floor bath; water stain in toilet closet on 

1st floor

No bath fan in bath; possible 

water leakage

10 Window sills on west side of house showing mold and moisture 

damage on drywall around frames and interior sill; occupants 

reported condensation during early winter months

Nothing out of the ordinary

13 Mold on caulk around most windows; occupants reported 

condensation during early winter months

Small fountain in living room

14 Mold on caulk around windows; moisture stains on drywall around 

frames; occupants reported condensation on windows during early 

winter months; mold on caulk in bathrooms and on exterior siding

Nothing out of the ordinary

17 Mold, window sills and air handler; mostly on north side of house Unknown

20 Wet plywood under air handler Condensation on air-conditioning 

drain line

Climate: Cold

21 Damp basement Occasional water leakage

22 Mold on storm windows; some condensation on sills; mold in 

shower (bath fan present)

Unknown

27 Damp basement Occasional water leakage

29 Condensation on basement walls Lots of water seepage into space; 

crawlspaces with dirt floors

30 Musty smells, 2nd floor; insulated behind built-ins; smell gone Unknown

32 Mold in upstairs bathroom (fan present) Unfinished basement with stone 

foundation open to house

35 Musty smell upstairs Moisture in basement

36 Water in basement Occasional water leakage

38 Some mold in mechanical room on drywall near floor; occupants 

were remodeling and fixing air leaks and replacing windows

Previous plumbing leakage into 

basement ceiling; was being 

repaired

39 Mold in bathrooms Very low bath-fan flows; two dogs 

and a fish tank

Climate: Marine

44 Mold on windows (all single-pane windows) Partial dirt crawlspace; vapor 

barrier not well installed

45 Mold on ceiling and windows in bathroom Partial dirt crawlspace; no bath 

fan

46 Mold on bathroom ceiling (fans present) Fireplace: gas insert; no damper 

in flue per code; no doors 

49 Mold on windows (single-pane windows) and bath ceiling (fan 

present—timer on 10 minutes)

Two fish tanks, four rabbits, and 

two dogs; some foundation 

leakage during heavy rain

56 Windows: mold where glass meets sash (dble-pane, low-e, vinyl) Vented crawlspace

57 Mold in upstairs bath and on windows (single-pane windows) No bath fans

61 Mold in 2nd floor bath Bath fans present but do not work
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The engineers analyzed these data sets more closely to determine if their humidity ratios were 

notably different from the other homes in their region. Again, correlations between indoor 

humidity and the following characteristics were examined: • Number of occupants.

• Occupant density.

• Foundation type.

• Air leakage. • Mechanical ventilation.

• Presence of bath fans.

First, the engineers compared the average monthly interior humidity ratios for the homes with

moisture problems with the homes without moisture problems. The results for Zone 2 show 

increased humidity levels from October through April for the homes with moisture problems. In 

Zone 4, the interior humidity levels were slightly higher for the homes with problems and the 

foundation humidity levels were significantly higher. Zone 5 showed significantly higher levels 

in both the interior and the foundation humidity levels compared with those homes without 

reported moisture problems. Figures 20 through 22 display the monthly average humidity 

ratios for the homes with reported moisture problems and for those without moisture problems 

for each zone. The graphs for Zones 4 and 5 show the foundation humidity ratios compared 

with the interior humidity ratios for both cases.

Figure 20. Interior Humidity Ratios for Homes With and Without Moisture Problems in 

Zone 2

0.007 

0.0075 

0.008 

0.0085 

0.009 

0.0095 

0.01 

0.0105 

Jan  Feb  Mar  Apr  May  Jun  Jul  Aug  Sep  Oct  Nov  Dec 

Humidity Ra0o 

Interior Humidity Ra0os for Homes w & w/out 

Moisture Problems:  Zone 2 

Zone 2 Int Problems  Zone 2 Int OK 
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Figure 21. Humidity Ratios for Homes With and Without Moisture Problems in Zone 4

Figure 22. Humidity Ratios for Homes With and Without Moisture Problems in Zone 5

0.0035 

0.0045 

0.0055 

0.0065 

0.0075 

0.0085 

0.0095 

0.0105 

Jan  Feb  Mar  Apr  May  Jun  Jul  Aug  Sep  Oct  Nov  Dec 

Humidity Ra0o 

Humidity Ra0os for Homes w & w/out Moisture 

Problems:  Zone 4 

Zone 4 Int Problems  Zone 4 Int OK  Zone 4 Found. Problems  Zone 4 Found. OK 

0.0035 

0.0045 

0.0055 

0.0065 

0.0075 

0.0085 

0.0095 

0.0105 

0.0115 

Jan  Feb  Mar  Apr  May  Jun  Jul  Aug  Sep  Oct  Nov  Dec 

Humidity Ra0o 

Humidity Ra0os for Homes w & w/out Moisture 

Problems:  Zone 5 

Zone 5 Int Problems  Zone 5 Int. OK 

Zone 5 Found. Problems  Zone 5 Found. OK 
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Problems Encountered and Lessons Learned

Because this federally funded survey was subject to the Paperwork Reduction Act, a delay 

occurred while waiting for the Office of Management and Budget to approve the amount of 

paper being used in the survey instrument. This delay resulted in a delay in recruiting 

candidate households, which was done as a courtesy to avoid having to reschedule the initial 

site visits and thereby inconveniencing the candidates. After the OMB approved the survey 

instruments, the engineers began selecting candidates and scheduling initial site visits.

At the beginning of the project, the biggest difficulty was finding participants who would agree 

to have their homes inspected for several hours for the initial site visit and to be monitored for 

a full year. In Florida, finding willing volunteers was so difficult that the engineers changed the 

parameters for selecting participants: they increased the size of the homes allowed in the 

study from 3,000 square feet to 3,500 square feet, used more block homes than was originally 

intended, and allowed a couple of the participants’ homes with only one occupant. Although it 

was originally thought that builders who had worked or were currently working with the 

engineers? would be good sources of leads for candidates, this expectation did not turn out to 

be the case. Most of the builders were concerned that such a study could cause them legal 

problems if moisture problems were detected in homes they had constructed.

Overall, the engineers did not encounter many problems during the data collection period.

Three occupants decided to do some level of remodeling, the effects of which will be 

investigated. One sensor was lost (most likely to vermin) and a few stopped collecting data 

along the way. In a couple of instances, the occupants forgot where the sensors were located 

and moved furniture that had loggers stuck to the back. In general, the participants were very 

cooperative and many were involved in helping to recruit others at the beginning of the study.

Interpretation of the conditioned building envelope proved to be challenging in some cases 

simply because of the way the occupants used the home. Unlike performing an energy rating 

on new construction, existing homes must be evaluated based on how the occupants use and 

condition the space, or the analysis will not make sense. For instance, if an engineer considers

an unfinished basement as unconditioned, but the occupants leave the door open most of the 

time for pets or children, it would be wrong to remove that space from the air-leakage analysis 

and conditioned square footage calculation. Because these conditions were some of the major 

characteristics being analyzed for their effects on interior humidity levels, proper

characterization was important to this study. The engineers combined their professional 

experience with the occupant interviews to determine what spaces they should consider 

conditioned versus unconditioned.

Among the biggest challenges of this study were (1) analyzing a large data file (almost 10 

million records total with five data points for each record) and (2) finding significant correlations 

from so few homes across such unique climates. Quickly and efficiently analyzing that amount 
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of data proved to be challenging using Microsoft ACCESS. Although the engineers conducted 

the initial review with ACCESS, they later decided to use MathWorks MATLAB software for the 

more critical work of identifying correlations.

Conclusions

Of the three climates in the study, the homes in Zone 4, the marine Northwest climate, appear 

to consistently have indoor relative humidity levels above 50 percent, but the highest level of 

moisture problems occurred in Zone 5, the cold Northeast climate. 

After conducting an initial review of the data, the engineers noted that major differences 

between the housing sets appear to include the following:

• Age. Homes in the marine climate were much older than homes in the other two 

climates. • Air leakage. The air-leakage rate (ACH50) was almost twice as high in the marine 

climate than in the other two climates. • Foundation type. Several homes in the marine climate were built on vented 

crawlspaces with dirt floors. Homes in the hot-humid climate were all built on slabs, and 

homes in the cold climate were primarily built on partially finished basements that were 

conditioned.

• Cooling equipment. Only 20 percent of the homes in the marine climate had central 

air-conditioning units, whereas 75 percent of the homes in the cold climate and 100

percent of the homes in the hot-humid climate had central air-conditioning.

• Heating equipment. Only about 50 percent of the homes in the marine climate had 

forced-air heating. The other 50 percent of the homes had a mixture of boilers with 

baseboard radiators or electric heat. 

In all three regions, the highest occurrence of visible mold or moisture damage was on or 

around windows and in the bathrooms. In the hot-humid climate, mold was visible on several 

air handlers around the cooling coil, usually on air handlers located outside the conditioned 

space such as in a garage.

In the cold climate, moisture problems included musty smells within the conditioned space that 

were reported by the occupants and confirmed on site. Several homes had water leakage 

problems in the basements. In a couple of instances these smells were noted on the upper 

floors of the home as opposed to the basements.

The engineers were not able to establish strong correlations between house characteristics 

and indoor humidity levels because of the small sample size. After evaluating the data 

regionally, across regions, and with respect to those homes that did and did not have moisture 

problems, the engineers determined that trends that deserve further investigation are related to 

increased humidity levels due to the following: 
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• High air-change rates. • High-occupant densities.

• The presence of unfinished and unconditioned basements and crawlspaces. • The use of materials with higher condensation and mold potential in climate Zone 2,

such as metal windows and marble window sills.

For a large study, independent loggers proved to be a reliable, cost-effective method for 

collecting data. Less than a 2-percent loss of data was realized in 285 separate loggers.

Although an interim site visit was required to download data half way through the study, ease 

of installation, accuracy, and cost of equipment outweighed the costs associated with the travel 

and hours necessary to conduct the extra site visit.

The engineers’ analysis of the homes with and without moisture problems in each climate zone 

led to the following conclusions.

Zone 2, Hot-Humid Southeast

For homes in this climate, mold was visible on several air handlers around the cooling coil, 

usually on air handlers located outside the conditioned space, such as in a garage. The 

humidity ratios are not much different between the homes with moisture problems and the 

homes without. The moisture problems seem to be occurring during the late fall and early 

winter in the newer homes that are more efficient and have a lower air-change rate. This result

could be due to the fact that these homes are more comfortable and require less heating 

during those months, allowing the interior relative humidity levels to rise.

Zone 5, Cold Northeast

Many of the homes in this climate with moisture problems had moisture leakage in the 

basement and all had a portion of the basement that was unfinished. Musty smells within the 

conditioned space were reported by the occupants and confirmed on site. In a couple of 

instances these smells were noted on the upper floors of the home rather than in the 

basements. Homes with moisture problems in this region also have a much higher average airchange rate compared with those homes without moisture problems. The engineers suggest 

that the effect of foundation moisture on the interior humidity levels should be investigated.

Zone 4, Marine Northwest

The homes in this climate were considerably older than homes in the other two climates. A

significant number of homes in this region had dirt crawlspaces, some vented and some not.

The homes with moisture problems all had a crawlspace or partial crawlspace and none had a 

well-sealed vapor barrier over the dirt. Other notable differences were the air-change rates and 

the occupant densities. The engineers suggest that all these characteristics should be 

investigated further to determine their influence on interior moisture levels.
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The engineers conclude that each region appears to have problems specific to that location 

that should be addressed. They also suggest not making blanket recommendations for 

moisture control across zones.
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References

American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE), Inc.

2009. ANSI/ASHRAE Standard 160-2009. “Criteria for Moisture-Control Design Analysis in 

Buildings.” Atlanta: American Society of Heating, Refrigerating and Air-Conditioning Engineers, 

Inc.

Aoki-Kramer, Michael, and Achilles Karagiozis. 2004. “A New Look at Residential Interior 

Environmental Loads.” In: Proceedings, Performance of Exterior Envelopes of Whole Buildings 

IX [CD-ROM]. Atlanta: American Society of Heating, Refrigerating and Air-Conditioning 

Engineers, Inc.

Dacquisto, David, Jay Crandell, and James Lyons. 2004. Building Moisture and Durability: 

Past, Present, and Future Work. Washington, DC: U.S. Department of Housing and Urban 

Development.

Kalamees, Targo. 2006. Indoor Hyrgothermal Loads in Estonian Dwellings. The 4th European 

Conference on Energy Performance and Indoor Climate in Buildings, 20–22 November 2006, 

Lyon, France.

Kumaran, Kumar, and C. Sanders. 2008. IEA Annex 41: Whole Building Heat, Air, Moisture 

Response, Subtask 3: Boundary Conditions and Whole Building HAM Analysis. Paris, France: 

International Energy Agency, Executive Committee on Energy Conservation in Buildings and 

Community Systems.

Piggs, Scott. 2003. Field Study of Ventilation in New Wisconsin Homes. Madison, WI: Energy 

Center of Wisconsin.

Rudd, Armin, and Hugh Henderson. 2007. Monitored Indoor Moisture and Temperature 

Conditions in Humid-Climate US Residences. Transactions DA-07-046. Atlanta: American 

Society of Heating, Refrigerating and Air-Conditioning Engineers, Inc.

TenWolde, Anton. 2000. Mold and Decay in TriState Homes, Durability and Disaster Mitigation 

in Wood-Frame Housing. Madison, WI: Proceedings of the 2nd Annual Conference PATH 

Consortium for Wood-Frame Housing in cooperation with The Forest Products Society.

TenWolde, Anton, and Iain Walker. 2001. Interior Moisture Design Loads for Residences. 

Buildings VIII/Whole Buildings––Principles. Atlanta: American Society of Heating, Refrigerating 

and Air-Conditioning Engineers, Inc.
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Additional Reading

Arens, Edward, and Anne Baughman. 1996. “Indoor Humidity and Human Health—Part I: 

Literature Review of Health Effects of Humidity-Influenced Indoor Pollutants,” ASHRAE 

Transactions 102 (1): 193–211.

American Society for Testing and Materials (ASTM). 2007. ASTM E1554 - 07. “Standard Test 

Methods for Determining Air Leakage of Air Distribution Systems by Fan Pressurization.” West 

Conshohocken, PA: American Society for Testing and Materials.

American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE), Inc. 

2004. ANSI/ASHRAE Standard 55-2004. “Thermal Environmental Conditions for Human 

Occupancy.” Atlanta: American Society of Heating, Refrigerating and Air Conditioning 

Engineers, Inc.

———. 2009. 2009 ASHRAE Handbook––Fundamentals. American Society of Heating, 

Refrigerating and Air-Conditioning Engineers, Inc.

Çengel, Yunus A., and Michael A. Boles. 1989. Thermodynamics: An Engineering Approach. 

New York: McGraw-Hill, Inc.

Cornick, Steven, Madeleine Rousseau, and Marianne Manning. 2008. Laboratory Testing 

Protocols for Exterior Walls in Canadian Arctic Homes. Ottawa, Ontario, Canada: National 

Research Council Canada, Institute for Research in Construction.

Emmerich, Steve, Andrew Persily, and Steve Nabinger. 2002. Modeling Moisture in 

Residential Buildings With a Multizone IAQ Program. Proceeding: Indoor Air, 2002.

Gaithersburg, MD: National Institute of Standards and Technology, Building and Fire Research 

Laboratory.

Francisco, Paul W. 2006. Field Performance Update of the Delta-Q Test for Residential Duct 

Leakage. Austin, TX: Affordable Comfort, Inc.

Hens, Hugo. 1996. Modeling, Final Report. IEA Annex 24, Task 1, ACCO. Leuven, The 

Netherlands: International Energy Agency.

Kreider, Jan, and Ari Rabl. 1994. Heating and Cooling of Buildings: Design for Efficiency. New 

York: McGraw-Hill, Inc.

Modera, Mark, and Jeanne Byrne. 1997 (January/February). “Can a New Duct Test Take the 

Pressure?” Home Energy 14 (1). 
Visit PD&R’s website 

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42

Moyer, Neil, Dave Chasar, Dave Hoak, and Subrato Chandra. 2004. Assessing Six Residential 

Ventilation Techniques in Hot and Humid Climates. Publication FSEC-PF-378-04. Cocoa, FL: 

Florida Solar Energy Center.

National Research Council Canada, Institute for Research in Construction (NRC-IRC). 2007. 

Findings From Carmacks Yukon Survey. PERD 079-Task 1. Ottawa, Ontario, Canada: 

National Research Council Canada.

Piggs, Scott. 2006. A Field Study of Ventilation in Wisconsin Weatherization Assistance 

Program Homes. Madison, WI: Energy Center of Wisconsin.

Walker, Iain, Darryl Dickerhoff, and Max Sherman. 2002. The Delta Q Method of Testing the 

Air Leakage of Ducts. LBNL-49749. Berkeley, CA: Lawrence Berkeley National Laboratory.

Walker, Iain, Max Sherman, Jacob Wempen, Duo Wang, Jennifer McWilliams, and Darryl

Dickerhoff. 2001. Development of a New Duct Leakage Test: Delta Q. LBNL-47308. Berkeley, 

CA: Lawrence Berkeley National Laboratory.
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APPENDIX A. PRESCREENING QUESTIONNAIRE
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APPENDIX B. FIELD DATA COLLECTION FORM
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46
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47

APPENDIX C. MONITORING AGREEMENT