Residential Housing Prices Research Articles

Government expenditure and the housing puzzle: Unpacking mechanisms

Shocks in housing demand can trigger economic downturns, prompting governments to boost public consumption to mitigate its impact. The literature has found conflicting effects of government spending on residential investment and housing prices. We hypothesize that differences in the construction sector’s responses may be due to factors unaccounted for in the estimations, such as labor supply response to wage changes, labor reallocation between sectors, and the amount of household debt. Using a dynamic general equilibrium model, we demonstrate that government consumption boosts tradable goods production, resulting in increased labor demand and wages. However, this diverts workers away from construction, potentially deepening the wound in this sector. Specific labor market attributes and economic indebtedness can catalyze worker displacement from the construction sector, thus adversely affecting residential investment and overall credit. As a result, borrowers face considerable welfare losses. Redirecting government spending toward housing sustains residential activity while exacerbating the overall welfare decline. Our analysis provides plausible explanations for the disparate empirical evidence on the impact of government spending on the construction sector.

Will the Release of Presale Housing moderate or exacerbate residential price volatility? - Taking Apartmemt complexes Units in Taichung City as an Example

During periods of economic prosperity, increased profit opportunities lead to active transactions in the existing housing market and heightened consumer risk preferences. At this time, developers increase the launch of presale housing projects to expand business opportunities, indirectly filling the supply gap of existing homes and alleviating housing demand. However, in reality, prices in both the presale and existing housing markets mutually drive each other up, further delaying the stabilization of housing prices. Therefore, whether the launch of presale housing projects can alleviate the demand for existing homes and achieve the function of price moderation becomes a research-worthy question. This study aims to investigate whether the volume of presale housing projects moderates or exacerbates housing prices. We focus on the volatility of aggregate residential housing prices in Taichung City, Taiwan, using transaction data of monthly average prices for Apartmemt complexes Units (high-rise buildings and high-rise apartments) from August 2012 to June 2023. We employ the ARCH/GARCH models to analyze the impact of presale housing project volumes on housing price volatility. Stability and convergence indicate a moderating effect of presale housing project volumes on housing prices; otherwise, it indicates exacerbation. After model testing, this study selects the most appropriate model as the ARIMA(3,1,0)-GARCH(1,0) model. This study finds that during periods of economic prosperity, the volume of presale housing projects does not aid in stabilizing or converging residential housing prices; instead, it exacerbates market disruption.

Neighborhood externalities modify the nexus between residential land and housing prices: A joint analysis using regression tree models

Neighborhood externalities influence residential land prices and housing prices simultaneously, contributing to a nexus relationship between them. Most existing studies have focused on the causal relationship between the two, with limited attention to their nexus. This research matched land parcel transactions from 2003 to 2015 and housing transaction records with purchasers’ housing purchase records before 2019 to construct a spatial-temporal land-housing dataset in Shanghai. Both spatial statistical tools and regression tree models were employed to explore the underlying mechanism, focusing on proximity to industries. According to the modeling results, residential land prices and housing prices are highly sensitive to polluting industries and producer service industries. The impacts of polluting industries on residential land prices have intensified, but the impacts on housing prices have not. The regression tree models further showed that living service industries were the primary positive contributor to residential land and housing prices. Regarding the social environment, non-local people are taking over communities with high-priced housing on high-value residential land parcels. These research outcomes encourage more efforts to explore the determinants of the nexus between residential land prices and housing prices, rather than discussion of the causality relationship.

Price Model for Transit-Oriented Developments in Kuala Lumpur, Malaysia

Abstract The idea of Transit Oriented Development (TOD) was to foster urban development around railway networks and has been strategically built and applied since the late 19th and early 20th centuries. Previous studies reported positive, negative, or irrelevant impact of TOD on prices of surrounding real estate. The study aims to evaluate the impact of TOD on property prices in Kuala Lumpur. It utilizes secondary data obtained from the National Property Information Centre (NAPIC), Malaysia, after data cleaning, Nine Thousand Five Hundred and Forty-Nine (9549) Housing Transactions between the periods 2009 and 2018 were used. The research design was quantitative, and the Hedonic Price Model (log-log model) was used for data analysis. The model revealed a multiple correlation coefficient (R) of 0.891 and an adjusted R2 value of 0.794, indicating that 79.4% of the house price variation is explained by the model. The F value of 996.921, which is statistically significant, indicates that the predictors significantly combine to predict the price of TOD areas in Kuala Lumpur. The coefficient for LnTrainsta is 0.056, indicating that there was a positive relationship between residential house prices and TOD in Kuala Lumpur; this explains that house prices increase by 5.6% for every 100 meter closer to the rail transit station.

Ecosystem service values provided by National Parks to residential property owners

This paper focuses on the ecosystem service benefits to homeowners from living in close proximity to three National Parks. Knowing these ecosystem service benefits is of policy and management relevance as expansion of existing Parks and protected areas along ecosystem boundaries often requires acquisition of undeveloped private land or other multiple use public lands, something that local officials may oppose. While the effects on house prices from urban parks, and two other types of U.S. Federal lands have been studied, the effect of lands in the U.S. National Park System on house prices had not been studied at the time of this research. To fill this information gap, hedonic price models were estimated to calculate the effect on residential house prices of proximity to three National Parks (NPs). After controlling for other characteristics of the house, neighborhood, and several other types of open space (e.g., National Forests, County Parks), we estimate house sale prices were on average 9.8% higher for houses within 2 km of National Park boundaries. This house price premium represents the value of the ecosystem services received by homeowners from living in close to these NPs. Quantifiable information on local ecosystem service benefits may ameliorate some concerns by local governments about converting private land or other agencies' multiple use lands in order to expand NPs or protected areas to better reflect ecosystem boundaries rather than historical, and often arbitrary boundaries. In addition, quantifiable information on enhancement of property values may broaden stakeholder groups to include the real estate industry. Thus, our results may support agencies in better achieving their objectives of managing NPs as ecological units.

Impacts of street tree abundance, greenery, structure and management on residential house prices in New York City

Urban trees have been widely linked to residential house prices. However, most existing studies focus on using tree abundance and greenery measures to evaluate the economic benefits of urban trees, while disregarding the effects of tree structure and tree-related management such as intentional maintenance and stewardship. We hypothesize that tree abundance, greenery, structure, and tree-related management are associated with house prices through offering local benefits that accrue to private homeowners and providing desirable living environments. We take street trees in New York City (NYC) as a case study to test the hypothesis. We first derived street tree canopy cover from high-resolution satellite images, street greenery from Google Street View, and street tree structure (e.g., species diversity, tree size, and tree health) and streetscape management variables (e.g., tree stewardship, tree guards that are a man-made structure to protect trees and enhance tree growth, and sidewalk damages adjacent to street trees) from the 2015–2016 street tree census data in NYC. We then built spatial hedonic price models to examine the associations between the above-mentioned street tree variables and residential house prices, after controlling for a variety of covariates. We found that HPMs that incorporate street tree structure and streetscape management offer stronger explanatory power than the one with only street tree abundance. Among the street tree-related variables we considered, tree canopy cover, green view index, tree stewardship and tree guards are statistically associated with increased house prices, while there are no such relationships for species richness, trees’ health status and streetscape sidewalk damages. More research is needed to understand the potential mechanisms and causal pathways that urban tree abundance, structure, and management affect residential house prices.

Hedonic analysis of willingness to pay for dam removal: evidence from Plymouth, Massachusetts

This study investigates how the removal of small dams, and subsequent river restoration investments, affect residential housing prices in Plymouth, Massachusetts, selected for its unusually high density of dam removals. From 2002 to 2019, 5 dams within 1.05 miles of each other have been removed. Following removal, riverine restoration was completed, leading to improved public safety for downstream infrastructure, fish passage, and better water quality. Yet, contention exists in local government decision-making from fear of loss of property values of homes that are no longer classified as “waterfront.” To examine that charge, we evaluate sale prices of 9,710 single-family homes from 1997 to 2021, in a comparative estimation of a hedonic regression and a difference in differences model. The results indicate that the price of homes sold in Plymouth after dam removal increased, with no impact on property values of waterfront and properties within 1 mile of riverine restoration locations. For properties within a 5-mile radius of a former dam, we observe comparative difference in price of a fraction of a percent for lots around 2 of the dams, in relation to all home lots in Plymouth. We explain that this difference is due to property-specific features, rather than the impact of dam removal. The implications are that dam removal would not negatively impact waterfront property values, while it may translate into an overall environmental price premium for the community at large.

COVID–19 Impacts: Government Interventions and Residential Housing Prices in Lagos Metropolis

The outbreak of the COVID-19 pandemic had profound impact on global economies. Various studies have focused on the impact of COVID-19 interventions on the financial market such as stocks markets, but few studies examined government interventions influence on housing market, particularly the housing price. This paper explores the influence of the government interventions during COVID-19 on housing prices in the residential areas of Lagos Metropolis in Nigeria. The aim is to understand whether price variations can be attributed to the epidemic shocks, by employing a review of literature on government interventions for COVID 19 pandemic and housing prices (2009 & 2015) before the pandemic and 2020 Lagos Property Market Consensus Report during the pandemic. Descriptive statistics were deployed in the application of difference-in-difference method for data analysis. The findings revealed that a lesser percentage of housing price increase was associated with the decrease in the number of COVID-19 cases and less partiallockdown measures. While a higher percentage housing price increase was associated with an increase in the number of COVID-19 cases along with more stringent full lockdown measures. The study concluded that housing price change was influenced by government interventions during COVID-19 pandemic in the Lagos Metropolis. The study recommends that to reduce the spread of COVID-19 infections in order to ensure a lesser percentage increase in housing price, spacious and more affordable housing units should be encouraged. Therefore, the effectiveness of lockdown and restriction measures should be further assessed, given their impacts on individual liberties and housing affordability.

Mortgage Credit and House Price Nexus: Evidence from Kenya

The purpose of this study was to examine the effect of mortgage credits on house prices in Kenya. Using correlational research design, the study employed a time series secondary data set for 2004 Q1 to 2017 Q4. This study found out that in the long-run, the mortgage loan (ß3 = 0.133144, p=0.0) has a positive and significant effect on residential house prices in Kenya. 1% increase in mortgage credit will cause a 0.13% increase in residential house prices. This study further established that any deviation from the long-run equilibrium is corrected at a speed of 40% in the next period. Therefore, the government should focus on policies that increase house supply, while to investors, the speed of adjustment does not seem to encourage arbitrage profit.

Spatial-Temporal Analysis of Residential Housing, Office Property, and Retail Property Price Index Correlations: Evidence from Ten Chinese Cities

Using correlation-based hierarchical analysis and synchronization analysis, this study focuses on monthly price indices for residential homes, office buildings, and retail properties in ten major Chinese cities for the years 2005 to 2021. Through these analyses, one can identify interactions and interdependence among the price indices, heterogeneous patterns in synchronizations of the price indices, and their evolving paths with time. Empirical findings suggest that the degree of real estate price comovements across all property types and cities is relatively low and stable from January 2017 to February 2020, followed by significant increases during the COVID-19 pandemic from March 2020 to January 2021 and significant decreases since February 2021 with the recovery of the economy. Several groups of property types and cities are determined in this study, each of which having its members reveal rather strong but volatile synchronizations of price indices. Rolling importance analysis does not suggest persistent increasing or decreasing trends for the real estate price associated with a specific property type and city. Policy studies on real estate price comovements may benefit from these findings here.

A more accurate estimation with kernel machine for nonparametric spatial lag models

Ignoring potential spatial autocorrelation in georeferenced data may cause biased estimators. Furthermore, existing studies assume insufficiently flexible structure of spatial lag model for some practical applications, which makes it difficult to portray the complex relationship between responses and covariates. Thus, we propose a novel garrotized kernel machine estimation method for the nonparametric spatial lag model and develop an eigenvector spatial filtering algorithm with sparse regression to filter spatial autocorrelation out of the residuals. The “one-group-at-a-time” cyclical coordinate descent algorithm is introduced for a solution path of tuning parameters. Our method can better describe the potential nonlinear relationship between responses and covariates, making it possible to model high-order interaction effects among covariates. Numerical results and the analysis of commodity residential house prices in large and medium-sized Chinese cities indicate that the proposed method achieves better prediction performance compared with competing ones. The result of real data analysis can provide guidance for the government to take targeted suppression measures of house prices for different areas.

A Proposal for a Residential Housing Price Index in Cyprus Through Analysis of Transaction-Based Data and Comparison With Existing Indices

This research suggests improvements to the macroeconomic housing indices of a thin real estate market, such as that of Cyprus, by testing various index construction methods with transaction-based data. Authors employ around 80% of the total number of apartment transfers documented at the Department of Lands and Surveys (DLS) of Cyprus, spanning from the first quarter of 2015 to the second quarter of 2022. They utilize this data to generate comprehensive indices at both the national and district levels. Authors studied, analyzed, and identified the deficiencies of the DLS database and tested the sample with six different methods. Log-linear time dummy hedonic models were found to explain the variation of prices better than other methods, mainly due to their ability to handle the diversity of properties in terms of location and physical characteristics and proposed techniques to deal with the issues of the standard time dummy (STD) and rolling time dummy (RTD) methods, regarding index revisions and low transaction volume during periods of downturns, respectively. Furthermore, a hybrid dependent variable of actual and appraised prices, that is, the accepted price, extracts explicit significantly better statistical measures. Additionally, the overall model fit was enhanced by introducing locality dummy variables and, through different combinations of attributes, captured the optimal results per district. Eventually, when the introduced transaction-based indices were compared to the corresponding existing published indices, which are based on non-actual data, we saw some resemblances, but overall, there were wide deviations.

Citizen actions in touristic neighbourhoods: Barcelona as a case study

ABSTRACT Touristification transforms the urban life of many tourist destination cities, leading to changes in the uses and values of city real estate, and generating a displacement of the most vulnerable sectors of the population. The objective of this research is the analysis of citizens’ strategies for facing this transformation process, considering that the associative and protest tradition in neighbourhoods can dampen (and this is our hypothesis) the intensity and effects of touristification. Therefore, this work examines the responses to the touristification processes of two Barcelona city neighbourhoods: Barceloneta and Hostafrancs. The results show that these areas resist and preserve the neighbourhood identity thanks to the drive and proactivity of its associative and cooperative fabric, both social and commercial, together with some political instruments proposed by the local government; albeit with different manifestations and results. The analysis of in-depth interviews, complemented by data on the number of tourist accommodation and the evolution of residential housing prices, constitute the empirical basis for carrying out this work.

RESIDENTIAL SPATIAL DIFFERENTIATION AT THE COMMUNITY LEVEL IN HANGZHOU, CHINA

Residential spatial differentiation is an important social issue for every large city to explore. The “subcultural” clusters formed by the subjective choices of a city’s residents divide the living spaces of the city and create residential differentiation. A study of this phenomenon would help to facilitate the allocation of urban resources and the formulation of relevant development strategies. In recent years, Hangzhou, one of the major developed cities in China, has seen a gradual increase in the proportion of its non-local population. Employing principal component analysis and K-means clustering, this study investigated the city’s spatial differentiation of living spaces at the community level and identified the social clustering areas. The Shannon-Wiener index and the index of dissimilarity were used to examine the degree of residential segregation in each administrative district. The results showed that the functional distinctions among the districts of Hangzhou were pronounced and had certain degrees of class solidification. A certain spatial coupling was observed between residential differentiation and housing prices. More reasonable allocations of social resources would help to ameliorate the negative effects caused by the spatial differentiation of living spaces.

Essential notes on modeling rental rate for islamic home financing in Indonesia

Homeownership hitherto remains a formidable work in every country. Rental rate that comprises house and rental price is an arduous factor to determine just price to own the home.  This study attempts to unveil some essential notes for Islamic bank in undertaking rental rates for Islamic mortgage financing in Indonesia. By employing the qualitative approach of pedigree on Islamic mortgage financing, this study found some pertinent elements such as the rental price of residential housing, macroeconomics variables, and its heterogeneity and homogeneity factors are among the essential notes to undertake by relevant stakeholders in determining rental rate. The novelty of this study can be considered as the one that initiated to an exploration of rental rates as an alternative element in Islamic home financing in Indonesia. This study implies government policy to consider rental rate mechanism as a new approach to derive just Islamic home financing in Indonesia. In addition, Islamic banks will incorporate this approach as part of their pricing component in determining Islamic home financing.  Keywords: Rental Rate, Islamic Home (mortgage)Financing, Macroeconomics, Heterogeneity, Homogeneity.

Empirical evidence on the ownership and liquidity of real estate tokens

To better understand the potential and limitations of the tokenization of real asset markets, empirical studies need to examine this radically new organization of financial markets. In our study, we examine the financial and economic consequences of tokenizing 58 residential rental properties in the US, particularly those in Detroit. Tokenization aims at fragmented ownership. We found that the residential properties examined have 254 owners on average. Investors with a greater than USD 5,000 investment in real estate tokens, diversify their real estate ownership across properties within and across the cities. Property ownership changes about once yearly, with more changes for properties on decentralized exchanges. We report that real estate token prices move according to the house price index; hence, investing in real estate tokens provides economic exposure to residential house prices.

Network analysis of comovements among newly-built residential house price indices of seventy Chinese cities

PurposeUnderstandings of house prices and their interrelationships have undoubtedly drawn a great amount of attention from various market participants. This study aims to investigate the monthly newly-built residential house price indices of seventy Chinese cities during a 10-year period spanning January 2011–December 2020 for understandings of issues related to their interdependence and synchronizations.Design/methodology/approachAnalysis here is facilitated through network analysis together with topological and hierarchical characterizations of price comovements.FindingsThis study determines eight sectoral groups of cities whose house price indices are directly connected and the price synchronization within each group is higher than that at the national level, although each shows rather idiosyncratic patterns. Degrees of house price comovements are generally lower starting from 2018 at the national level and for the eight sectoral groups. Similarly, this study finds that the synchronization intensity associated with the house price index of each city generally switches to a lower level starting from early 2019.Originality/valueResults here should be of use to policy design and analysis aiming at housing market evaluations and monitoring.

Pursuing educational equality and divergence in the housing market: How do educational equality policies affect housing prices in Shanghai?

Property prices being linked to education creates and perpetuates issues of inequality. This study explores the effect of education equality policies on housing prices using data on existing housing transactions in Shanghai. Based on the propensity score matching (PSM) and difference in difference (DID) models, we find a significant phenomenon of educational capitalization, with residential housing prices in high-quality school districts being significantly higher than those in neighboring districts by 5–19 %. The policy shock of simultaneous admission to private and public schools further exacerbates residential price divergence. The results prove that the policy aims to promote educational equity may increase educational capital premiums and lead to another form of inequality in the housing market, which requires further attention.

Research on Prediction of Housing Prices Based on GA-PSO-BP Neural Network Model: Evidence from Chongqing, China

Since 2000, the real estate industry has experienced rapid development, and at the same time, it has driven the rapid growth of housing prices, and the trend of housing prices has attracted attention. This paper integrates genetic algorithm and particle swarm algorithm to optimize BP neural network, and establishes a housing price prediction model based on mixed genetic particle swarm BP neural network. The average data of housing prices in Chongqing, China from 2000 to 2020 and several main factors affecting the trend of housing prices were selected as experimental data. Through the training and simulation prediction based on the mixed particle swarm BP neural network, the error between the predicted value and the actual value was within 0.5%, the validity and accuracy of the model are proved. At the same time, this paper predicts the average price of residential commercial housing in Chongqing in 2021, which provides a reference for the government’s macro-control and sellers to carry out residential commercial housing.

Urban Heritage and its Socioeconomic Impacts in Seoul, South Korea: An Empirical Study Using Residential Environmental Satisfaction and Housing Price as Indicators

Background and objective Recent research has highlighted the need for urban heritage conservation due to rapid urbanisation, especially in Asian cities; however, few studies have investigated the socioeconomic impacts of heritage assets in urban contexts. This study examined urban heritage sites in Seoul Metropolitan City from the perspective of residents’ environmental satisfaction and housing prices. Methods A spatial regression model was developed to examine the associations between urban heritage sites and their corresponding protected areas as the independent variables (nationally assigned cultural heritage, city-assigned cultural heritage, nationally registered cultural heritage, nationally assigned cultural heritage protected area, and city-assigned cultural heritage protected area) and residential environmental satisfaction and housing prices as the dependent variables. The model investigated how urban heritage sites influence housing prices through the mediating effect of residential environmental satisfaction. Results The results confirmed the impact of urban heritage sites on housing prices and the mediating effect of residential environmental satisfaction. Moreover, depending on their urban heritage classifications, noticeable differences were evident in the impact of urban heritage sites. Conclusion These findings provide an intellectual foundation for public policies, offering insights into how they might achieve an optimum balance between private and public interests in matters of heritage conservation.