Housing Values Research Articles

Geographies and determinants of rent burden: A regional economic analysis of the U.S. metropolitan landscape

Rental housing affordability, a growing topic in interdisciplinary scholarship, remains relatively peripheral to geography and regional science. This article focuses on geographies of rent burden and factors affecting it at the metropolitan level in the conterminous U.S. By conducting cartographic and regression analyses, we examine relationships between rent burden and numerous related aspects derived from scholarly work. Besides offering a new measurement of rent burden, we put a special emphasis on regional economic specializations as potential predictors of rent burden’s intensity. The relationship between these two have not been studied in existing scholarship. Our results indicate that the most consistent determinants of more intense rent burden in numerous models include higher housing values and poverty rates, substantial shares of racial/ethnic minorities, and family structure represented by lower percent married. Regarding economic specializations, we find that manufacturing is a strong predictor of lower rent burden in most models, with its effect demonstrating an opposite direction—higher rent burden occurs in metropolises not specializing in manufacturing. Simultaneously, metropolises with substantial concentrations of employment in (i) education and medicine and (ii) arts, entertainment, and recreation exhibit higher rent burden and most of these metropolises are mid- and small-sized.

Spillover effects of business improvement districts on residential housing values: a focus on the northern region in Los Angeles county, California

ABSTRACT In response to the decline in commercial areas, there has been a growing interest in the implementation of Business Improvement Districts (BIDs) in large developed cities in North America. However, there is limited understanding of whether BIDs have economic effects on surrounding neighbourhoods. This study aims to answer the following research question: do BIDs positively affect nearby residential properties? To analyze the influence of BIDs on the housing market, we examined single- and multi-family home sale transactions between 1995 and 2007 in the City of Los Angeles, California. We employed the Adjusted Interrupted Time Series Difference in Differences (AITS DID) model to analyze changes in both the price levels and trends of housing values, and to establish the causal impact associated with the implementation of BIDs. Our findings show that the implementation of BIDs had a significant impact on the value of properties located within a 1/2 mile of the BIDs in single- and multi-family homes, as well as in low-income neighbourhoods. The findings from this study will provide valuable insights for planners, residents, and local governments to better comprehend the heterogeneous nature of spillover effects associated with BID programs.

Optimism in Property Markets

House valuation involves forecasting household demand for a neighborhood, whereas commercial property valuation involves forecasting area economic activity. Yet, we show optimistic expectations for both. We investigate further possible disparities due to different cultures of home ownership, short, medium, and long-term investment horizons, and bullish or bearish trends. We use an experimental framework to demonstrate expectations are optimistic under all conditions. Buyers use trends to surmise future prices in bull markets, expect price increases in oscillating markets, and price reversals in bear markets, failing to anticipate price busts. Policy implications are important in preventing property boom-induced financial crises.

Affordability of Owner-Occupied Housing across US Cities

This paper compares the affordability of owner-occupied housing across US cities. We apply a new method for imputing the rental value of owner-occupied housing developed in Diamond and Diamond (2024) that accounts for unobserved house quality. On average, the rental value of owner-occupied housing is 3.2 percent above that implied by standard hedonic methods. Hedonic methods overestimate rental value of owner-occupied housing in the most regulated cities and sharply underestimate it in the least regulated cities. We provide suggestive evidence that this heterogeneity reflects misallocation, where high-quality housing stock cannot be built in the most desirable parts of regulated cities.

Racial and Ethnic Representation in Local Government

Does the presence of underrepresented racial/ethnic groups in a legislative body differentially impact outcomes for members of those groups? We study close elections between White and non-White candidates for California city council and the corresponding impact on housing values, a summary statistic for neighborhood investment. We find electing non-White rather than White candidates generates differential home value gains in majority non-White neighborhoods. This result, which is not explained by correlations between candidate race and political affiliation or neighborhood racial composition and income, suggests that increased representation can reduce racial disparities. Our results strengthen with increased city-level segregation and council member pivotality. (JEL D72, J15, R23)

Methodology in Assessing Value of Traditional Houses in the Northwest of Vietnam for Sustainable Development and Conservation

Methodology in Assessing Value of Traditional Houses in the Northwest of Vietnam for Sustainable Development and Conservation

The spatial polarization of housing wealth accumulation across Spain

Housing wealth is central to structuring inequalities across societies. Processes of financialization have intensified the speculative nature of housing, while labour and welfare restructuring increase the importance of property wealth towards economic security. Housing, however, represents an exceptional asset given its inherently spatial nature and buy-in barriers. This implies that not only access to homeownership but where households enter the housing market is central to wealth trajectories. Spatial inequality in housing trends thus fundamentally structures wealth dynamics. While some scholarship has posited increasing housing market spatial polarization, there remains a lack of empirical evidence. This research turns to the context of Spain, to directly assess spatial polarization in housing value accumulation. Employing an innovative dataset at a detailed geographic scale, the analyses reveal strong increases in polarization across the national territory over the past decade. Strikingly, these dynamics appear resistant to major upheavals, including the post-GFC crash and Covid-19 impacts, and are robust across scales. The analyses reveal that more expensive areas saw greater absolute gains and higher rates of appreciation. The findings expose a structural intensification of spatial polarization and provide crucial empirical evidence of how the housing market acts in amplifying inequality through the spatial sorting of wealth accumulation.

Machine Learning based Predicting House Prices using Regression Technique

Predicting the price of a house helps for ascertain the house's selling price in a specific area and assist individuals in determining the ideal moment to purchase a home. Our goal in this machine learning task on house price prediction is to use data to develop a machine learning model capable of predicting housing values in the specified area. We will implement a linear regression algorithm on our dataset. By using real world data entities, we are going to predict the price of the house in that area. For better results we require data pre-processing units to increase the model's efficiency for this project we are using supervised learning, which is a part of machine learning. We have to go through different attributes of the dataset. This project provides us an overview on how to predict house prices using various machine learning models with the help of different python libraries. This suggested model is thought to be the most accurate one for estimating home prices and makes the most accurate predictions. This offers a succinct overview, which is necessary in order to forecast the price of the home. This project consists of what and how the house price model works with the assistance of machine learning technique using scikit-learn and which datasets we will be using in our proposed model. Key Words: house price, lasso regression, ridge regression, R-squared.

The NB Housing Summit: Solutions to Promote Housing Affordability in the Mid-sized City of Saint John, New Brunswick

Canada is in the midst of a national housing crisis that renders households vulnerable to unstable and unaffordable housing, and various forms of homelessness. New Brunswick is a largely rural Canadian province with three mid-sized cities that have lower housing values than larger Canadian centres. However, affordability is eroding in the province’s housing market, and the rental market offers few options for low-to-moderate-income renters. Simultaneously, visible homelessness and encampments are on the rise. Saint John, one of the mid-sized cities, is a relatively new destination for housing market investment and interprovincial migration, which is leading to higher rents and housing purchase prices. Through an analysis of World Café data collected with 85 individuals (representatives from government, people with lived experience of housing instability, non-profit agencies, housing developers, etc.) in Saint John, New Brunswick, this paper presents locally driven, on-the-ground solutions to the housing and homelessness crises in a mid-sized city.

Quantifying the Impact of the COVID-19 Pandemic on Housing Prices in Los Angeles using Time Series Analysis

The real estate market is an indispensable part of both the national economy and the financial system. For households, housing is one of the most important consumer commodities, and a family’s finances and expenses are bound to be significantly influenced. For a country, the housing market majorly affects the growth of the GDP, so even a small fluctuation in the real estate market can lead to a subsequent impact on the economy as a whole. Therefore, it is worthwhile to take housing price forecasts into consideration. In addition, with the outbreak of COVID-19, the impact this pandemic brings to the real estate market needs to be analyzed. As a result, this research focuses on making a forecast of housing prices in Los Angeles by applying the ARIMA model and then quantifying how the COVID-19 affects housing values using time series analysis. In that case, policymakers can make more informed regulations on the real estate market, making citizens' lives easier than before.

The social lives of rental proptech: Entanglements between capitalist, care and techno-utopian values

Housing’s value is contested, with discussions including the economic value of housing through literature on assetisation and financialisation, social value in debates about housing as a human right, value capture in infrastructure development, and the value of housing for social reproduction. With this in mind, we engage with the various ways housing, tenants and Proptech are valued by tenant advocates, real estate professionals, and proptech developers in Sydney, Australia. This is not as a reductive exercise to find ‘the value’ of housing or Proptech, but to recognise and engage with the ways various social, digital and financial valuations of housing, tenants and digital technologies inform a situated and relational politics of Proptech value. As such we advance a ‘more-than-political’ economy of Proptech. To illustrate this conceptual case, we discuss three regimes of rental Proptech value in Australia – capitalist and economic regimes; social capital through an ethic of care; and techno-utopian values, where technology is deployed on the basis that it will solve a presumed problem. Our empirical focus is on the application of Proptech to longer-term rental market, which is emerging in less visible ways than its highly contested counterparts in the short-term rental sector.

Housing prices and environmental hazards: the effects of industrial park openings in Korea

ABSTRACT We explore an empirical examination on the effects of industrial park openings on housing prices in Korea under under hedonic framework. First, the availability of multiple transactions within apartment complexes enables fixed-effects estimation and addresses unobserved heterogeneity across apartment complexes. Second, we trace the effect on housing values from the announcement stage to actual operation stage of industrial parks. Our results indicate housing prices positively respond to an announcement of industrial park opening, but less in areas close to the industrial park. Furthermore, housing prices in close proximity to industrial parks decline when industrial parks begin operation.

Targeting performance of Dibao program in China: An empirical study based on the 2020 China Family Panel Studies

The rural minimum living security (Dibao) Program is regarded as an essential means to alleviating absolute poverty in rural areas. This study uses logistic regression to identify the influencing factors for the distribution of Dibao subsidies based on the CFPS 2020 database covering 28 provinces in China. Then, we categorize the sample into four types of households to further analyze the targeting performance of Dibao program with the method of multiple logit regression. The results reveal that the living standards of households, such as public facilities, drinking water, cooking fuel, housing value, and assets can impact the distribution and targeting performance of Dibao subsidies. Having educational burdens can significantly increase the probability of being covered by Dibao program for non-low-income households. Large family-size households that are involved in agricultural-related work and use electricity or other fuels for cooking are more likely to receive Dibao subsidies. When compared with the eastern region, households in the western region are more likely to gain Dibao subsidies. The results of propensity scores reveal that the contemporary mismatch of the rural minimum living security system has been overestimated in many research by simply treating non-low-income households that receive Dibao. However, the inclusionary targeting errors are still high (76%), which might indicate that there are still shortcomings in the targeting mechanism for the rural minimum subsistence allowance system. The research findings generate useful implications for policymakers to improve the accuracy in allocating rural Dibao subsidies.

CIM-WV: A 2D semantic segmentation dataset of rich window view contents in high-rise, high-density Hong Kong based on photorealistic city information models

Large-scale assessment of window views is demanded for precise housing valuation and quantified evidence for improving the built environment, especially in high-rise, high-density cities. However, the absence of a semantic segmentation dataset of window views forbids an accurate pixel-level assessment. This paper presents a City Information Model (CIM)-generated Window View (CIM-WV) dataset comprising 2,000 annotated images collected in the high-rise, high-density urban areas of Hong Kong. The CIM-WV includes seven semantic labels, i.e., building, sky, vegetation, road, waterbody, vehicle, and terrain. Experimental results of training a well-known deep learning (DL) model, DeepLab V3+ , on CIM-WV, achieved a high performance (per-class Intersection over Union (IoU) ≥ 86.23%) on segmenting major landscape elements, i.e., building, sky, vegetation, and waterbody, and consistently outperformed the transfer learning on a popular real-world street view dataset, Cityscapes. The DeepLab V3+ model trained on CIM-WV was robust (mIoU ≥ 72.09%) in Hong Kong Island and Kowloon Peninsula, and enhanced the semantic segmentation accuracy of real-world and Google Earth CIM-generated window view images. The contribution of this paper is three-fold. CIM-WV is the first public CIM-generated photorealistic window view dataset with rich semantics. Secondly, comparative analysis shows a more accurate window view assessment using DL from CIM-WV than deep transfer learning from ground-level views. Last, for urban researchers and practitioners, our publicly accessible DL models trained on CIM-WV enable novel multi-source window view-based urban applications including precise real estate valuation, improvement of built environment, and window view-related urban analytics.

Modelling Wealth and Wealth Inequality in Bulgaria

This paper analyses net wealth and net wealth inequality determinants in Bulgaria, for the 2000-2020 period. The econometric results suggest that the introduction of corporate and individual flat tax rates in 2007 and 2008 contribute to households’ net wealth creation but have no significant association with inequality variables. Interest rates on mortgage loans, cost of debt, budget surpluses and inflation have a negative association with wealth, while the value of houses, arable land, equity, interest income, budget deficits contribute to net wealth growth. The top percentile increases its wealth share supported by the value of houses, equity and positive current account to Gross Domestic Product (GDP) values, however the cost of debt and GDP per capita are in a negative association with top percentile inequality. The bottom half of the population increases its wealth share with higher GDP per capita values and higher consumer loans rates, however inflation affects it negatively. Inflation and investment income are in a positive association with the Gini coefficient, while interest rates on consumer loans, cost of debt, GDP per capita, Government revenues, dividend income, equity, and the value of houses reduce inequality.

Who Captures the Value from Organizational Ratings?: Evidence from Public Schools

Ratings of organizations and firms have become ubiquitous. These ratings, often produced by intermediaries (including private and public organizations), are designed to aid consumers and other stakeholders in their decision making while guiding rated organizations toward performance improvement or compliance. In doing so, these intermediaries introduce new information to markets. However, disparities may exist in the ability to strategically capture the value from such ratings, often due to differential access to complementary assets among stakeholders. Consequently, this differential ability can lead to outcomes contrary to the rating institutions’ intentions. Reflecting on this dynamic, we analyze how widespread access to a prevalent type of rating—school performance information, often produced to enhance transparency and equity in educational access—has affected existing economic and social disparities in America. We leverage the staged rollout of GreatSchools.org school ratings from 2006 to 2015 to answer this question. Across various outcomes and specifications, we find that the availability of school ratings has accelerated the divergence in housing values, income distributions, education levels, and racial and ethnic composition across communities. Affluent and more educated families were better positioned to strategically leverage this new information to capture educational opportunities in communities with top schools. The uneven benefits we observe highlight how ratings can unintentionally deepen existing inequalities, thereby complicating their intended impacts. Supplemental Material: The online appendix is available at https://doi.org/10.1287/stsc.2023.0113 .

Interest Rates and the Spatial Polarization of Housing Markets

Rising within-country differences in house values are a much-debated trend in the United States and internationally. Using new long-run regional data for 15 advanced economies, we show that standard explanations linking growing price dispersion to rent dispersion are contradicted by an important stylized fact: rent dispersion has increased far less than price dispersion. We propose a new explanation: a uniform decline in real risk-free interest rates can have heterogeneous spatial effects on house values. Falling real safe rates disproportionately push up prices in large agglomerations where initial rent-price ratios are low, leading to housing market polarization on the national level. (JEL E43, R21, R31)

Using benefit transfer to estimate housing value increases from improved water clarity: A case study of lakes in Kosciusko County, Indiana.

This study provides step-by-step guidance for practitioners and local stakeholders on how to use existing study results to conduct benefit transfer (BT), and ultimately make informed predictions of how improvements in lake water clarity may benefit surrounding communities. The procedures are demonstrated using a publicly available meta-dataset developed by the United States Environmental Protection Agency, and a subsequent meta-analysis that synthesizes the literature on how improvements in water clarity impact home values. The BT procedures are demonstrated using a case study of 14 large lakes in Kosciusko County, Indiana. Lake-specific average increases in home values, as well as the value of the housing stock in aggregate, are calculated for illustrative improvements in lake water clarity. This analysis provides a critical bridge to better connect high-quality, academic research with real-world policy analysis, and ultimately serves to better equip local governments and stakeholders to make more informed policy and land use decisions.

Influence of urban-form and sociodemographic factors on street tree abundance transpire across scales in Houston, TX

Benefits provided by street trees can be enjoyed by all urban residents but trees along these public rights-of-way are usually unevenly distributed in a city. Both urban-form and sociodemographic factors can influence the spatial distribution of street trees. However, little is known how the relative importance of urban form and sociodemographic characteristics on street tree abundance inequalities vary across neighborhoods. In this study, we explored spatially varying influences of urban-form and sociodemographic variables on the distribution of street trees across Houston, Texas. We used three measures (green view index, street tree canopy, and street tree density) to measure street tree abundance. We then built multiscale geographically weighted regression (MGWR) models to capture the spatial heterogeneity in the influence of urban form and sociodemographic factors on street tree abundance. Our analysis indicates that the effects of urban-form and sociodemographic determinants on street tree abundance disparities operate at varying spatial scales across Houston. Overall, ‘building coverage ratio’ and ‘percent residential land use’ play primary roles in shaping the distribution of street tree abundance in east Houston while effects of “percent renter occupied” and “median house value” are more pronounced in west Houston. Our results also show that urban-form and sociodemographic factors interact with each other at spatial scales ranging from a few neighborhoods to several districts to the entire city. These scale- and location-specific nuances in our findings provide important insights to develop the kinds of urban planning strategies to attain increasingly equitable distribution of street tree abundance across an urban area.

A Room in the Film Capital: The Social Economy of Lodging and Urban Change in Hollywood during the 1930s

In Central Hollywood, during the 1930s, population became denser, housing values declined, and rooming houses increasingly defined much of the neighborhood. The lodging population was mostly white and native-born (but with significant Asian minorities), young, transient, and maritally unattached. They constituted a working class that was often precariously employed in the entertainment industry, the service sector, and other unskilled or semi-skilled occupations. The rooming houses and residential hotels provided the possibility of housing for the unemployed, the poorly paid, the temporary resident, and the elderly. Lodgers formed part of a socially diverse population in a neighborhood that offered opportunities for employment, services, and entertainment, usually within walking distance. During a decade when migration to Los Angeles was still considerable, rooming houses provided a flexibility in housing possibilities that would decline after the Second World War.