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Research Theme: Housing & Homelessness

Includes social and real-estate implications of homelessness, housing affordability and livability in the built environment

Factors Influencing Teleworking Productivity – a Natural Experiment during the COVID-19 Pandemic

Shi, Xiao; Moudon, Anne Vernez; Lee, Brian H. Y.; Shen, Qing; Ban, Xuegang (Jeff). (2020). Factors Influencing Teleworking Productivity – a Natural Experiment during the COVID-19 Pandemic. Findings.

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Abstract

Of 2174 surveyed adults who were teleworking following the implementation of a Covid-19 work-from-home policy, 23.8% reported an increase in productivity, 37.6% no change, and 38.6% a decrease in productivity compared to working at their prior workplace. After controlling for feelings of depression and anxiety likely caused by pandemic-related circumstances, the socioeconomic characteristics associated with no change or an increase in productivity after shifting to teleworking included being older; not employed in higher education; having lower education attainment; and not living with children. Respondents with longer commute trips in single-occupancy vehicles prior to teleworking were more likely to be more productive but those with longer commute by walking were not. Lifestyle changes associated with increased productivity included better sleep quality, spending less time on social media, but more time on personal hobbies.

Rachel Berney and Jeff Hou contribute to new book on social justice in urban design

“Just Urban Design: The Struggle for a Public City” (MIT Press 2022) features a collection of chapters and case studies that apply a social justice lens to the design of urban environments. Sixteen contributors, including Rachel Berney of Urban Design & Planning and Jeff Hou of Landscape Architecture, examine topics ranging from single-family zoning and community capacity building to immigrant street vendors and the right to walk. The book is open-access and can be downloaded from MIT Press here.

Steven Bourassa

Steven C. Bourassa is H. Jon and Judith M. Runstad Endowed Professor and Chair of the Runstad Department of Real Estate in the College of Built Environments at the University of Washington. Previously, he served as department chair at Florida Atlantic University, the University of Auckland, and the University of Louisville, where he was KHC Real Estate Research Professor. His research focuses on urban housing and land markets and policy, covering a range of topics including housing tenure, residential property valuation, property taxation, housing affordability, low-income housing policy, community land trusts, and public land leasehold. He has published his research in numerous real estate and related journals, such as the Journal of Housing Economics, Journal of Real Estate Finance and Economics, Journal of Real Estate Research, and Journal of Urban Economics, as well as Real Estate Economics, Regional Science and Urban Economics, and Urban Studies. His co-edited book, Leasing Public Land: Policy Debates and International Experiences, was published by the Lincoln Institute of Land Policy. Dr. Bourassa is on the editorial boards of eight real estate journals. He is a Fellow of the Weimer School of Advanced Studies in Real Estate and Land Economics and received the Research Achievement Award from the International Real Estate Society, of which he is a past President. He is currently Treasurer of the American Real Estate and Urban Economics Association. He holds a Ph.D. in city and regional planning from the University of Pennsylvania.

Associations between Neighborhood Built Environment, Residential Property Values, and Adult BMI Change: The Seattle Obesity Study III

Buszkiewicz, James H.; Rose, Chelsea M.; Ko, Linda K.; Mou, Jin; Moudon, Anne Vernez; Hurvitz, Philip M.; Cook, Andrea J.; Drewnowski, Adam. (2022). Associations between Neighborhood Built Environment, Residential Property Values, and Adult BMI Change: The Seattle Obesity Study III. SSM-Population Health, 19.

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Abstract

Objective: To examine associations between neighborhood built environment (BE) variables, residential property values, and longitudinal 1-and 2-year changes in body mass index (BMI). Methods: The Seattle Obesity Study III was a prospective cohort study of adults with geocoded residential addresses, conducted in King, Pierce, and Yakima Counties in Washington State. Measured heights and weights were obtained at baseline (n = 879), year 1 (n = 727), and year 2 (n = 679). Tax parcel residential property values served as proxies for individual socioeconomic status. Residential unit and road intersection density were captured using Euclidean-based SmartMaps at 800 m buffers. Counts of supermarket (0 versus. 1+) and fast-food restaurant availability (0, 1-3, 4+) were measured using network based SmartMaps at 1600 m buffers. Density measures and residential property values were categorized into tertiles. Linear mixed-effects models tested whether baseline BE variables and property values were associated with differential changes in BMI at year 1 or year 2, adjusting for age, gender, race/ethnicity, education, home ownership, and county of residence. These associations were then tested for potential disparities by age group, gender, race/ethnicity, and education. Results: Road intersection density, access to food sources, and residential property values were inversely associated with BMI at baseline. At year 1, participants in the 3rd tertile of density metrics and with 4+ fast-food restaurants nearby showed less BMI gain compared to those in the 1st tertile or with 0 restaurants. At year 2, higher residential property values were predictive of lower BMI gain. There was evidence of differential associations by age group, gender, and education but not race/ethnicity. Conclusion: Inverse associations between BE metrics and residential property values at baseline demonstrated mixed associations with 1-and 2-year BMI change. More work is needed to understand how individual-level sociodemographic factors moderate associations between the BE, property values, and BMI change.

Keywords

Body-mass Index; Physical-activity; Food Environment; Socioeconomic-status; Weight-gain; Health; Quality

Differences in Weight Gain Following Residential Relocation in the Moving to Health (M2H) Study

Cruz, Maricela; Drewnowski, Adam; Bobb, Jennifer F.; Hurvitz, Philip M.; Moudon, Anne Vernez; Cook, Andrea; Mooney, Stephen J.; Buszkiewicz, James H.; Lozano, Paula; Rosenberg, Dori E.; Kapos, Flavia; Theis, Mary Kay; Anau, Jane; Arterburn, David. (2022). Differences in Weight Gain Following Residential Relocation in the Moving to Health (M2H) Study. Epidemiology, 33(5), 747-755.

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Abstract

Background: Neighborhoods may play an important role in shaping long-term weight trajectory and obesity risk. Studying the impact of moving to another neighborhood may be the most efficient way to determine the impact of the built environment on health. We explored whether residential moves were associated with changes in body weight. Methods: Kaiser Permanente Washington electronic health records were used to identify 21,502 members aged 18-64 who moved within King County, WA between 2005 and 2017. We linked body weight measures to environment measures, including population, residential, and street intersection densities (800 m and 1,600 m Euclidian buffers) and access to supermarkets and fast foods (1,600 m and 5,000 m network distances). We used linear mixed models to estimate associations between postmove changes in environment and changes in body weight. Results: In general, moving from high-density to moderate- or low-density neighborhoods was associated with greater weight gain postmove. For example, those moving from high to low residential density neighborhoods (within 1,600 m) gained an average of 4.5 (95% confidence interval [CI] = 3.0, 5.9) lbs 3 years after moving, whereas those moving from low to high-density neighborhoods gained an average of 1.3 (95% CI = -0.2, 2.9) lbs. Also, those moving from neighborhoods without fast-food access (within 1600m) to other neighborhoods without fast-food access gained less weight (average 1.6 lbs [95% CI = 0.9, 2.4]) than those moving from and to neighborhoods with fast-food access (average 2.8 lbs [95% CI = 2.5, 3.2]). Conclusions: Moving to higher-density neighborhoods may be associated with reductions in adult weight gain.

Keywords

Body-mass Index; Neighborhood Socioeconomic-status; New-york-city; Built Environment; Physical-activity; Food Environment; Urban Sprawl; Risk-factors; Obesity; Walking; Electronic Medical Records; Fast Foods; Population Density; Residential Density; Residential Moves; Supermarkets

Hedonic, Residual, and Matching Methods for Residential Land Valuation

Bourassa, Steven C.; Hoesli, Martin. (2022). Hedonic, Residual, and Matching Methods for Residential Land Valuation. Journal Of Housing Economics, 58.

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Abstract

• Our first method involves a hedonic model estimated for sales of vacant lots. • Another method depreciates improvements, obtaining land value as a residual. • Our third approach matches the sales of vacant and subsequently developed lots. • This allows us to estimate a hedonic model of land leverage (the ratio of land to total property value) for improved properties. • We conclude that the third approach is the most promising of the three methods. Accurate estimates of land values on a property-by-property basis are an important requirement for the effective implementation of land-based property taxes. We compare hedonic, residual, and matching techniques for mass appraisal of residential land values, using data from Maricopa County, Arizona. The first method involves a hedonic valuation model estimated for transactions of vacant lots. The second approach subtracts the depreciated cost of improvements from the value of improved properties to obtain land value as a residual. The third approach matches the sales of vacant lots with subsequent sales of the same properties once they have been developed. For each pair, we use a land price index to inflate the land price to the time of the improved property transaction and then calculate land leverage (the ratio of land to total property value). A hedonic model is estimated and used to predict land leverage for all improved properties. We conclude that the matching approach is the most promising of the methods considered. [ABSTRACT FROM AUTHOR]; Copyright of Journal of Housing Economics is the property of Academic Press Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Keywords

Hedonic Method; Land Leverage; Land Valuation; Matching Approach; Residual Approach

Housing Cost Burden and Life Satisfaction

Acolin, Arthur; Reina, Vincent. (2022). Housing Cost Burden and Life Satisfaction. Journal Of Housing & The Built Environment, 37(4), 1789-1815.

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Abstract

The share of income that households spent on their housing has been increasing over time in a wide range of countries, particularly among lower income households. In theory, the share of income spent on housing can reflect variations in household preferences for housing consumption but for low-income household, high burdens are likely more reflective of constraints and force these households to face tradeoffs between housing and non-housing consumption that negatively affect their overall life satisfaction. This paper uses data from the 2018 European Union Statistics on Income and Living Conditions (EU-SILC) for 14 countries. We find that, controlling for household sociodemographic characteristics, households spending more than 30 percent of their income and those spending more than 50 percent of their income on housing report significantly lower levels of life satisfaction. The estimated relationship is largest for this latter heavily cost burdened group. The negative relationship between housing cost burden and reported life satisfaction is found across countries but varies in magnitude, suggesting that stronger welfare systems may mediate the negative impacts of housing cost burdens, although further research is needed to confirm both this relationship and the precise mechanisms driving it.

Keywords

Life Satisfaction; Income; Housing; Poor Communities; Subjective Well-being (psychology); Living Conditions; European Countries; Housing Cost; Subjective Wellbeing; Economic Hardship; Homeownership; Affordability; Determinants; Cost Analysis; Housing Costs; Households; Consumption; Low Income Groups; Expenditures; Welfare; Sociodemographics

Measuring the Housing Sector’s Contribution to GDP in Emerging Market Countries

Acolin, Arthur;hoek-smit, Marja;green, Richard K. (2022). Measuring the Housing Sector’s Contribution to GDP in Emerging Market Countries. International Journal Of Housing Markets And Analysis, 15(5), 977-994.

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Abstract

Purpose > This paper aims to document the economic importance of the housing sector, as measured by its contribution to gross domestic product (GDP), which is not fully recognized. In response to the joint economic and health crises caused by the COVID-19 pandemic, there is an opportunity for emerging market countries to develop and implement inclusive housing strategies that stimulate the economy and improve community health outcomes. However, so far housing does not feature prominently in the recovery plans of many emerging market countries. Design/methodology/approach > This paper uses national account data and informal housing estimates for 11 emerging market economies to estimate the contribution of housing investments and housing services to the GDP of these countries. Findings > This paper finds that the combined contribution of housing investments and housing services represents between 6.9% and 18.5% of GDP, averaging 13.1% in the countries with information about both. This puts the housing sector roughly on par with other key sectors such as manufacturing. In addition, if the informal housing sector is undercounted in the official national account figures used in this analysis by 50% or 100%, for example, then the true averages of housing investments and housing services’ contribution to GDP would increase to 14.3% or 16.1% of GDP, respectively. Research limitations/implications > Further efforts to improve data collection about housing investments and consumption, particularly imputed rent for owner occupiers and informal activity require national government to conduct regular household and housing surveys. Researcher can help make these surveys more robust and leverage new data sources such as scraped housing price and rent data to complement traditional surveys. Better data are needed in order to capture housing contribution to the economy. Practical implications > The size of the housing sector and its impact in terms of employment and community resilience indicate the potential of inclusive housing investments to both serve short-term economic stimulus and increase long-term community resilience. Originality/value > The role of housing in the economy is often limited to housing investment, despite the importance of housing services and well-documented methodologies to include them. This analysis highlights the importance of housing to the economy of emerging market countries (in addition to all the non-GDP related impact of housing on welfare) and indicate data limitation that need to be addressed to further strengthen the case for focusing on housing as part of economic recovery plans.

Keywords

Pandemics; Economic Importance; Investments; Housing; Sanitation; Recovery; International Organizations; Covid-19; Economic Growth; Data Collection; Economic Indicators; Economics; Housing Conditions; Economic Policy; Economic Conditions; Market Economies; Resilience; Low Income Groups; Economic Activity; Consumption; Emerging Markets; Earthquakes; Surveys; Gross Domestic Product--gdp; Coronaviruses; Affordable Housing; Economic Development; Informal Economy; Households; Recovery Plans; Disease Transmission; Africa; South Africa; India