House Prices Research Articles

Clean Identification? The Effects of the Clean Air Act on Air Pollution, Exposure Disparities, and House Prices

Clean Identification? The Effects of the Clean Air Act on Air Pollution, Exposure Disparities, and House Prices

The Financial Consequences of Pretrial Detention

Abstract In the United States, a significant number of criminal defendants are held in pretrial detention and face substantial financial burdens. Matching individual-level criminal case records to household-level financial data, we exploit the quasi-random assignment of court commissioners to study how pretrial detention affects household solvency. We find that pretrial detention results in higher rates of household insolvency, driven by higher rates of chapter 7 bankruptcies and judgment liens, and higher foreclosure rates during periods of decreasing house prices. We document that the effects spill over to family members and show that home equity can cushion households from insolvency. (JEL D14, G51, K41, K42)

Judge Dread: Court severity, repossession risk and demand in mortgage and housing markets

Abstract We study the impact of borrower protection on mortgage and housing demand using variation in the likelihood that houses are repossessed coming from heterogeneity in preferences of local judges. We develop a framework that provides conditions to identify the impact of repossession risk on housing and credit demand – holding supply fixed. Empirically, we exploit exogenous variation in risk created by boundaries of courts’ catchment areas. We find that one standard deviation decrease in borrower protection decreases borrowing and house prices by 4.5%.

Information sharing across MLS platforms and housing prices: Evidence from a temporary suspension of an agreement

AbstractThis study examines a unique case of a suspension of a data‐sharing agreement between two multiple listing services (MLSs) in South Florida, and investigates the impact of such a (negative) shock on the trading outcomes of the MLS platforms. Using property listing data from the two MLSs, we find that sale prices were lower during the data suspension period for transactions involving properties that were in areas where the listing broker may rely on brokers from the other MLS to bring potential buyers. The negative price impact remains stable and consistent under various robustness checks. Furthermore, the results also reveal heterogeneous pricing and liquidity effects due to the data‐sharing suspension‐related shock.

The effects of community land trusts on neighborhood outcomes

AbstractWe examine the spillover effects of community land trusts (CLTs) on nearby house prices and demographic composition. CLTs are nonprofits that promote affordable home‐ownership by holding the land in trust and selling the dwellings to income‐eligible households. We build a new data set of property acquisitions from 15 major CLTs, combined with data on housing transactions, demographics, and migration histories over the period 2000–2016. Results show that acquisitions stabilize or increase nearby home prices, even as broader neighborhood prices decline, with no detectable effect on resident displacement. Our findings suggest that CLTs can play a role in stabilizing neighborhoods with declining property values.

Return of house price premiums based on proximity to subway stations in the post-pandemic period in Seoul, South Korea

Return of house price premiums based on proximity to subway stations in the post-pandemic period in Seoul, South Korea

Smart Renting: Harnessing Urban Data with Statistical and Machine Learning Methods for Predicting Property Rental Prices from a Tenant’s Perspective

The real estate market plays a pivotal role in most nations’ economy, showcasing continuous growth. Particularly noteworthy is the rapid expansion of the digital real estate sector, marked by innovations like 3D visualization and streamlined online contractual processes, a momentum further accelerated by the aftermath of the Coronavirus Disease 2019 (COVID-19) pandemic. Amidst this transformative landscape, artificial intelligence emerges as a vital force, addressing consumer needs by harnessing data analytics for predicting and monitoring rental prices. While studies have demonstrated the efficacy of machine learning (ML) algorithms such as decision trees and neural networks in predicting house prices, there is a lack of research specifically focused on rental property prices, a significant sector in Brazil due to the prohibitive costs associated with property acquisition. This study fills this crucial gap by delving into the intricacies of rental pricing, using data from the city of São Carlos-SP, Brazil. The research aims to analyze, model, and predict rental prices, employing an approach that incorporates diverse ML models. Through this analysis, our work showcases the potential of ML algorithms in accurately predicting rental house prices. Moreover, it envisions the practical application of this research with the development of a user-friendly website. This platform could revolutionize the renting experience, empowering both tenants and real estate agencies with the ability to estimate rental values based on specific property attributes and have access to its statistics.

Impact of Urban Expansion on School Quality in Compulsory Education: A Spatio-Temporal Study of Dalian, China

With rapid urbanization, improving school quality in compulsory education is critical for optimal educational resource allocation. This study integrates a random forest machine learning model, GIS spatial analysis, and a spatial econometric model to examine the spatiotemporal differentiation of school quality in Dalian, China, in 2016 and 2020, as well as its relationships with the construction land development cycle, population density, and housing prices. The findings reveal a core–periphery structure, with overall school quality on the rise and basic facility configuration exerting a stronger impact than teacher strength. Among internal resources, per capita sports venue area (PCSFA) and per capita teaching equipment value (PCTRE) contribute most significantly to school quality, while high-quality clusters in traditional educational hubs, university-covered areas, and transitional zones spur improvements in surrounding schools. The population density, housing prices, and the construction land development cycle all positively correlate with school quality, highlighting the need for coordinated action among urban planners, education authorities, and housing regulators to ensure that land development, housing affordability, and school facility investments advance equitable access to quality education. These results provide a novel perspective on compulsory education quality assessment and offer a valuable foundation for guiding education policies and urban development strategies.

Optimizing Polynomial and Regularization Techniques for Enhanced Housing Price Prediction Accuracy

This study investigates the effectiveness of various regression models for predicting housing prices using the California Housing dataset. The models evaluated include Linear Regression, Ridge Regression, Best Polynomial Ridge Regression, Lasso Regression, Elastic Net Regression, and Support Vector Regression (SVR) with an RBF kernel. The analysis reveals that SVR with an RBF kernel exhibits the poorest performance, characterized by the highest Mean Squared Error (MSE) and the lowest R² score, indicating limited effectiveness for this dataset. Conversely, Linear Regression, Ridge Regression, and Best Polynomial Ridge Regression demonstrate significantly lower MSE values and nearly identical R² scores, each explaining approximately 60% of the variance in housing prices. Among these, Best Polynomial Ridge Regression marginally outperforms Linear and Ridge Regression, suggesting that including polynomial features enhances model performance. Although Lasso Regression shows slightly higher MSE than the leading models, it still performs better than Elastic Net and SVR. Overall, the study identifies linear regression, ridge regression, and best polynomial ridge regression as the most effective models for this dataset, with SVR and an RBF kernel being the least effective.

Transcending the urban–rural dichotomy: inequality in urban green space availability among urban neighbourhoods, urban villages and rural villages in Guangzhou, China

ABSTRACT Urban green spaces (UGS) are integral to sustainable urban development, yet the urbanization process has resulted in unequal distributions of green resources across different neighbourhoods. In Chinese cities, there are three primary types of neighbourhoods with different socioeconomic features – rural villages (RV), urban villages (UV), and urban neighbourhoods (UN). They usually have different green space availability, posing challenges to environmental justice. However, the Green space inequality concerning neighbourhood types has not yet been fully explored. Drawing on the case of Guangzhou, we examine inequalities in UGS availability among these three types of neighbourhoods. We first explored the inequality in UGS availability among UN, UV and RV by employing the Gini and Theil indices and then used the eXtreme Gradient Boosting (XGBoost) model and the SHapley Additive exPlanation (SHAP) explainer to elucidate the intricate association between neighbourhood socioeconomic statuses and UGS availability from a local perspective. Our findings indicate that UN and UV have similar levels of inequality in UGS availability, which is more severe compared to RV. Neighbourhoods with high population density and sufficient job opportunities in UN and UV, as well as those with low housing prices, low-salary and low-educated jobs in UN and RV, are suffering from UGS inequality. This study unveils the spatial disparity of UGS inequality among different types of neighbourhoods, providing insights into equal green space planning and contributing to environmental justice.

Housing market, social housing, and fertility: evidence from urban China

ABSTRACT This study utilizes panel data from 283 Chinese cities between 2009 and 2017 and employs a fixed-effects model to analyze the effects of commercial housing prices and social housing supply on birth rates, considering the interaction between the housing market and social housing policies. The findings indicate that a 1% increase in housing prices results in a 0.063% decrease in the birth rate, while a 1% increase in social housing supply leads to a 0.075% increase in the birth rate. However, although increasing the supply of social housing can positively influence birth rates, its substitutability with commercial housing may reduce the availability of commercial properties, driving up housing prices and potentially lowering fertility rates. Therefore, an unlimited social housing supply policy may not effectively resolve the issue of low birth rates. To be more inclusive, local governments should adopt a demand-oriented social housing policy that addresses the diverse needs of families, rather than focusing solely on supply-side measures. Highlights Social housing supply in areas with lower housing prices positively affects birth rates. Social housing supply in areas with higher housing prices negatively affects birth rates. Among social housing types, economic and comfortable housing (ECH) has a greater impact on fertility rates than public rental housing (PRH). A demand-driven social housing supply policy is necessary for low-income households.

A Threshold Analysis of the Effect of House Prices on U.S. Local Labor Markets

This paper uses a threshold regression model to examine whether the effect on total employment of an exogenous change in tradable sector employment differs in systematic ways depending on the average house price. The results based on data from 321 U.S. metropolitan areas suggest the presence of a threshold effect. The local employment multiplier increases with the average house price until some house price threshold level beyond it decreases, suggesting that affordable local housing markets not only help attract companies but also allow local economies to maximize the benefits from location-based development policies aimed at creating more jobs.

Data-driven urban Development Prospects Based Research

This study evaluates the future development prospects of Changchun and Hohhot through a data-driven approach, focusing on key factors such as housing prices, service levels, urban resilience, and sustainable development. The research integrates advanced modeling techniques including multiple linear regression, random forest, and XGBoost to analyze various urban indicators. In housing, the models identify price fluctuation factors and estimate housing stock using indirect metrics, with gradient boosting regression emerging as the most effective model. Changchun is found to excel in retail, finance, and lifestyle services, while Hohhot excels in public infrastructure, though both cities require balanced development to address disparities in facility distribution. Urban resilience is assessed by examining response capabilities to extreme weather and emergencies, with targeted investments suggested to enhance resilience and service accessibility. Sustainable development strategies for both cities are proposed, with Changchun focusing on green transportation and mixed-use developments, and Hohhot emphasizing healthcare and tourism infrastructure. Both cities are encouraged to adopt smart city technologies to optimize resource allocation. The study concludes that tailored development strategies, informed by data-driven insights, are crucial for achieving balanced, resilient, and sustainable urban growth, with practical recommendations for policymakers to improve service equity and foster long-term urban vitality.

Economic policy uncertainty and housing prices: evidence from Northern and Southern Europe

Economic policy uncertainty and housing prices: evidence from Northern and Southern Europe

High-Quality Public Transport and its Association with House Prices

High-Quality Public Transport and its Association with House Prices

COVID-19 pandemic and housing demand mortgage: Partial adjustment model approach

One of the efforts to encourage the fulfillment of housing needs is the provision of housing both from the government and the private sector by considering the community's purchasing power. The background of this research is that the fulfillment of housing needs by both the government and the private sector has yet to be maximized while the demand for housing is relatively high. This research is still necessary because the demand for housing is still high. Increased housing demand will push house prices due to limited housing land. The problems faced focus on the relatively high demand for mortgages because even in the conditions of the COVID-19 pandemic, people still make houses their top priority. This study aims to analyze the effect of people's income, loan interest rates, the amount of housing finance, and dummy before and during the COVID-19 pandemic on housing demand mortgage. The data analysis technique in this study used multiple regression Partial Adjustment Model (PAM). The results showed that the variables of income as proxy for GDP and loan interest rates significantly affected housing demand mortgage. In contrast, the amount of housing finance and the COVID-19 dummy had no significant impact. The research implication that researchers can provide to the government is to provide policy recommendations regarding the Housing Finance Liquidity Facility (FLPP) program, which is intended for low-income communities.

Valuation of Urban Parks Under the Three-Level Park System in Shenzhen: A Hedonic Analysis

Urban parks play a pivotal role in fostering ecologically advanced cities in China, contributing significantly to enhancing urban environments and the overall quality of life for residents. However, their value is often hard to quantify by urban planners due to the absence of a clear market price. Using the hedonic price method, this study delves into the distinct impacts of different types of urban parks (community parks, city parks, country parks) on housing prices under the three-level park system in Shenzhen. The outcomes reveal that owning a view of both city parks and country parks from the apartment has significant positive influence on housing prices, with a premium of 14.8916% (RMB 920,396.73) and 30.7299% (RMB 1,899,309.15), respectively. Regarding accessibility, shortening walking distance by 100 m to the nearest community park and city park can attach a premium to the housing price by 0.3269% (RMB 20,203.55) and 0.6130% value gain (RMB 37,889.53), respectively. The results drawn from distinctive park types are expected to give insights to urban planners in formulating strategies for ecological civilization construction, with an emphasis on prioritizing visible and accessible greenspaces to cater to citizens’ preferences and foster sustainable urban growth.

Linking Housing Prices and Innovation: The Role of Commuting Distance?

Linking Housing Prices and Innovation: The Role of Commuting Distance?

MODELING HOUSE SELLING PRICES IN JAKARTA AND SOUTH TANGERANG USING MACHINE LEARNING PREDICTION ANALYSIS

The increasing demand for housing in urban agglomerations, particularly in areas like Jakarta, has made homeownership a significant challenge for many, especially first-time buyers and the lower-middle class. Post-pandemic shifts have further influenced housing preferences, driving interest towards suburban areas with green spaces. Despite government efforts through mortgage subsidy programs, affordability remains a concern, particularly in peripheral regions. This study aims to analyze housing prices in various Jakarta regions using machine learning models, including Multiple Linear Regression (MLR), Support Vector Regression (SVR), Light Gradient Boosting Machine (LGBM), and Random Forest. A dataset of 554 house prices from West Jakarta, South Jakarta, Central Jakarta, and South Tangerang was used. The analysis focused on key predictors like land area, building area, bedrooms, and carports, with R² and Mean Squared Error (MSE) metrics evaluating model performance. Results showed that LGBM and Random Forest outperformed others with 0.8 R2 and low MSE, with building and land area as the most significant factors influencing prices. The study concludes that property size is a primary determinant of house prices, and there is a need for policy interventions to make housing more affordable. Additionally, apartment rentals offer a viable alternative, especially in central urban areas, where proximity to economic activities and facilities is crucial. The findings suggest that enhancing marketplace features with predictive tools could further assist buyers in making informed decisions.

Impact of the war in Ukraine on the real estate market in Romania: Analysis in geopolitical context

The war in Ukraine has caused profound transformations in the dynamics of the Romanian real estate market, reflecting the impact of a complex combination of economic and financial, demographic and socio-cultural, as well as political and legal factors. In this context, the objective of the study is to analyze the impact of the war in Ukraine on the real estate market in Romania by assessing the influence of determinants to identify measures to support the stability and development of the real estate market within geopolitical crises. Subsequently, based on statistical analysis methods, the econometric model was developed considering as dependent variables the real estate market (measured by average price for housing) and independent variables – the number of Ukrainian migrants, interest rate on mortgage loans, GDP per capita, and inflation rate. The research results reveal that Ukrainian migration has a significant positive impact on the real estate market, leading to price increases for housing in urban areas and increased demand for rental housing. In addition, mortgage interest rates show a significant negative impact, suggesting that lower affordability of financing limits purchases of real estate. Moreover, GDP growth per capita is positively correlated with price dynamics in the real estate market, confirming that a prosperous economy stimulates investments in real estate. Findings provide valuable insights into understanding how geopolitical and economic factors interact to influence the real estate market.