Differences In Housing Prices Research Articles

『스마트상점 기술 도입』이 소상공인 점포의 매출과 만족도에 미치는 영향

This study decomposes the local gap through the oaxaca factor analysis to find out how differences in housing prices affect the management costs with data the average value of three-year management costs between 2014 and 2016 to 536 complexes in three Gangnam and Gangbuk districts in Seoul. The results of the analysis of 290 complexes in three Gangnam and 246 complexes in three Gangbuk districts are as follows. First, housing prices did not affect administrative costs within the region, but were variables that affected data pooling. Second, when compared with the three Gangnam districts, the results of the characteristic coefficient, which can be explained by the characteristics of the complex and the external environment, showed that the gap in the management cost increased when the number of security personnel, repair and maintenance costs, elevator maintenance cost, floor area ratio, and 60m2 or more to 85m2. Third, the factors behind the gap between Gangnam 3 districts, which have higher housing prices than Gangbuk 3 districts, were confirmed to be the number of security personnel, repair costs, floor area ratio and elevator maintenance costs, and the higher the size and income of their holdings, the more they lived in Gangnam 3 districts, where local infrastructure was expanded, causing social inequality due to differences in housing welfare, such as safety, comfort and convenience.

House prices and fertility: Can the Dutch housing crisis explain the post‐2010 fertility decline?

AbstractFertility has declined in many rich societies after 2010. However, the factors that explain this fertility decline remain poorly understood. In particular, little is known about how changes on the housing market contributed to the fertility decline. This study examines the links between house prices and fertility in the Netherlands, a country where house prices have risen dramatically in the past decade. Regional (NUTS 3) data for the period 1995–2022 are analysed using within‐between models. The results for the within‐effect of house prices show that after 2010, fertility decreased (more) in regions where house prices increased (more), suggesting that the housing crisis in the Netherlands can explain a substantial part of the post‐2010 fertility decline. Before 2010, however, increases in regional house prices were associated with small increases in fertility, showing that the direction of the within‐effect of house prices depends on the time period that is studied. Finally, differences in house prices between regions are not consistently associated with fertility.

Intra-urban house prices in Madrid following the financial crisis: an exploration of spatial inequality

In this paper we explore the temporal dynamics of spatial inequality in housing prices for Madrid, the capital city of Spain. Spatial inequalities are a concerning feature of urban areas across the globe. It has been suggested that within cities housing prices are becoming more geographically unequal over time, particularly since the 2008 housing market crash. However, more evidence is needed at the intra-urban level to understand neighbourhood house price differences in large urban areas. Changes are analysed during a key period of the housing market bust (2010–2015) and boom (2016–2019), using data from a major housing listing portal. Fine grain space-time analysis of the distribution of housing prices supports an increase in spatial inequality and polarisation at the neighbourhood level. Two spatially differentiated housing sub-markets of high- and low-priced housing are identified. The persistence and growth of spatial house price inequality has important societal implications for the wealth gap and segregation of rich and poor in cities.

Are gated communities “safe havens”? Examining housing price dynamics of Chinese gated and non-gated communities during COVID-19 pandemic

Gated communities (GCs) and associated socio-economic implications are of global concern. How GCs and non-GCs responded to the external shock of the pandemic has not been comprehensively understood. Drawing on the information of infected cases during the first wave pandemic (January-March 2020) and longitudinal data of housing sales and rental prices for each community from July/2019-June/2020 in 11 Chinese cities, we employ a hedonic difference-in-differences approach accounting for heterogenous effects in stagger timing to discover the effects of COVID-19 on housing price differences between GCs and non-GCs. Moreover, the buffer zones of infected epicentres are delineated to explore the spillover effects. Compared with GCs, non-GCs with different distances from the pandemic epicentres experienced significantly greater reductions in both housing sales and rental prices at varying degrees. While the effect on housing sales prices was significant over time, it only lasted for one month for rental prices. This study enhances our understanding of housing price dynamics and the socio-spatial implications of GCs.

Housing markets and geographical labour mobility to high-productivity regions: The case of Stockholm

In many countries, there are signs of declining migration to high-productivity urban areas due to restrictions in the housing market and increasing regional differences in housing prices. Using detailed population-wide register data for Sweden, we estimate how regional variation in housing prices and homeownership is associated with the individual’s decision whether to accept a job offer in the Stockholm metropolitan region and the interrelated choice between migration and commuting as the mobility mode. Our findings indicate that high relative housing prices in the Stockholm area and homeownership are associated with decreasing total geographical labour mobility to the region. This is pronounced among the young and among highly skilled workers. The negative effects of high relative housing prices and homeownership on migration are partially but not fully compensated by positive effects on commuting to Stockholm.

Impact of urban underground sewage treatment plants on surrounding housing prices: Case study of Beijing, China

Currently, the enhancement of the surrounding house prices and land value by underground construction of urban sewage treatment plants (USTP) is mainly limited to the qualitative level, lacking quantitative evaluation methods. Analysing the impacts of USTP on the surrounding housing prices, an above-ground sewage treatment plant (ASTP) and a USTP in Beijing are considered research objects, with the housing price data collected and processed utilising geographic information and big data technology. Using the difference-in-difference (DID) model, the impact of urban sewage treatment plants (STP) on the surrounding housing prices is quantitatively analysed. Analysing the differences in house prices, growth volume and growth rate before and after the underground construction of urban STPs makes it clear that the USTP has a positive promoting effect on the surrounding house prices within 0–1 km; however, ASTP negatively inhibits house prices of the same size. Given the ASTP inhibition, analysis of reconstruction in the underground mode on the surrounding housing prices and the value of the released land resources makes it clear that its economic value is much greater than the investment in itself and the operating costs. In addition, it was verified that the underground reconstruction of ASTPs in large cities could significantly improve the economic value of the surrounding areas.

The Formation Mechanism of House Price Differences in China’s Urban Area in the Same City—Based on Lasso Bayesian Model Averaging Method

With the development of economy and the expansion of urban scale, the variation of housing prices within Chinese cities has gradually become significant. Based on the housing prices of 79 districts in Chengdu, this paper analyzes the reasons for such differences. With an index system of the price difference of locational housing, a semi-log linear regression Lasso Bayesian average model is constructed, which can realize variable selection while estimating coefficients. Empirical evidence shows that the inequality of social-economic resources and the imbalance of housing supply and demand in the area are important internal factors that lead to the difference in housing prices between areas within the city; the related housing prices have an extremely significant positive impact, showing that the mobility of the place of purchase and the contagion of the house price are the main reasons for housing price; in addition, influential factors have strong positive interaction effects.

The impact of wildfire on property prices: An analysis of the 2015 Sampson Flat Bushfire in South Australia

This research investigates the impact of wildfire occurrence on the price of properties subject to wildfire risk. This is relevant considering anthropogenic climate change is increasing wildfire frequency, size, and severity and the expansion of wildland-urban interface is exposing more people to the costs associated with fires. The study addresses the methodological limitations of prior research by exploiting the difference-in-differences method and a comprehensive dataset comprising transaction data, location and neighbourhood amenity elements, and spatial data on wildfire risk area identification to analyse the impact of the 2015 Sampson Flat Bushfire on housing price differences between the designated Bushfire Prone Area (BPA) and non-BPA within the City of Mitcham in South Australia. The results show that houses in the BPA (i.e., facing bushfire risk) are sold at a 4.96 % discount compared to those within the non-BPA (i.e., not facing bushfire risk) after the fire, suggesting that wildfire risk perception significantly rises following the disaster. Based on a dynamic analysis, houses in BPAs experience an immediate discount of 4.55 % compared to non-BPAs in the first six months following the fire, and this effect continues for at least two years afterward. Most interestingly, a heterogeneity analysis unique to this paper shows that houses within the Medium-Risk BPA are more negatively impacted than those in the High-Risk BPA.

Determinants of Regional Disparities in Housing Prices: A Spatial Analysis of German Regions

Germany is characterised by heterogeneous regional development in various economic spheres, including housing markets. Differences in housing prices persist during decades, causing undesirable inequality, affecting migration and employment patterns. The purpose of this work is to identify regional factors which affect regional housing prices in Germany. The peculiarity of the study is the consideration of the spatial location of regions for analysing the influence of the characteristics of neighbouring regions. Based on data from 397 German regions for 2004-2019, spatial econometric panel data models are built, which consider both selling and rental prices. The following factors that affect demand in the housing market are used as determinants of housing prices: the unemployment rate, the balance of pendulum migration at the place of work and living, the share of employment, wages, the number of employees, gross regional product. The analysis findings revealed that factors that raise income for the population trigger an upsurge in demand and prices for housing. Conversely, opposite effects result in a decline in prices due to a decrease in demand. Moreover, it was verified that neighbouring regions mutually affect housing markets through pendulum migration and the creation of economic clusters with similar living standards and prices. Furthermore, changes in labour market metrics are crucial; unemployment, wages, and the number of workers in nearby regions have a significant influence on real estate prices in the area under examination. The study’s practical importance lies in the possibility of using its outcomes to develop regional and migration policies.

Research on the Impact of Hangzhou’s Allocation Policies on School District Housing Based on Machine Learning Algorithms

This paper focuses on the impact of Hangzhou’s allocation policies on school district housing. Using a multistage sampling method and stratified sampling method, the researchers selected a sample size and survey subjects from the original six districts in Hangzhou. A structured questionnaire and Likert scale method were used to design the survey questionnaire, and in-depth interview questions were designed based on the principles of in-depth interviews. After completing the distribution and acquisition of the questionnaire and the cleaning of the relevant data, the researchers used the multiple logistic regression model to study the significance of the eight factors that affect parents’ intention to buy a house, and then used various machine learning classification methods such as KNN, decision tree, Boosting, Bagging, and random forest, and calculated the influence of the influence variables on the intention to buy a house using 10-fold cross-validation. Based on this analysis, housing price differences in the three months before and after the policy were analyzed, and related policy recommendations were put forward.

Research on Housing Price in Boston

In the United States, due to the characteristics of sparsely populated areas, its housing prices are not quite the same as those in China. At the same time, due to factors such as the freedom of firearms in the United States, many people consider more factors in purchasing houses, resulting in great differences in housing prices in the United States. The housing price problem has also become a major concern.
 This paper first analyzes fourteen variables, respectively, thirteen independent variables were analyzed to find the relationship between independent variables and housing prices, establish regression model analysis, and then through the frequency histogram to find the relationship between independent variables and frequency, through the relationship between frequency and housing prices to illustrate the relationship between independent variables and housing prices. By analyzing the correlation to find out the relationship between the variables and housing prices.
 Through the statistical description and analysis model of data, we can establish the normal distribution relationship between independent variables and dependent variables, and approximate the normal distribution parameters through the average and variance of independent variables. The relationship between each independent variable and dependent variable is fitted, and the influence of each variable on housing prices is finally obtained.

Research on Housing Price in Boston

In the United States, due to the characteristics of sparsely populated areas, its housing prices are not quite the same as those in China. At the same time, due to factors such as the freedom of firearms in the United States, many people consider more factors in purchasing houses, resulting in great differences in housing prices in the United States. The housing price problem has also become a major concern.
 This paper first analyzes fourteen variables, respectively, thirteen independent variables were analyzed to find the relationship between independent variables and housing prices, establish regression model analysis, and then through the frequency histogram to find the relationship between independent variables and frequency, through the relationship between frequency and housing prices to illustrate the relationship between independent variables and housing prices. By analyzing the correlation to find out the relationship between the variables and housing prices.
 Through the statistical description and analysis model of data, we can establish the normal distribution relationship between independent variables and dependent variables, and approximate the normal distribution parameters through the average and variance of independent variables. The relationship between each independent variable and dependent variable is fitted, and the influence of each variable on housing prices is finally obtained.

Neighbourhood stigma and place-based policies

Abstract We analyse the effects of the Dutch Act on Extraordinary Measures for Urban Problems. This allows local governments to prohibit non-employed households from entering into public housing in targeted neighbourhoods to improve social mixing. We show that the Act is largely ineffective in changing the demographic composition of neighbourhoods. At the same time, due to prominent advertising of targeted deprived neighbourhoods, a stigma may have been created. We adopt a hedonic price approach and use a boundary-discontinuity (within 100 m of neighbourhood borders) to quantify the overall effect of the policy. We thus exploit spatio-temporal differences in house prices and find a sizeable price reduction of about 3–5%. The magnitude of this effect is confirmed for two other national place-based policy programmes, adding to the external validity of these findings. Our results suggest that neighbourhood stigma is important, which implies that individuals living in deprived neighbourhoods experience dis-utility from living in a place with a low status.

Economic benefits of remediating the White Lake and Muskegon Lake Areas of Concern

This study uses hedonic modeling to estimate the property value benefits of remediation in the White Lake and Muskegon Lake, MI Areas of Concern (AOC). Both sites had long histories of industrial contamination; in the case of White Lake, this included tannery waste, and in the case of Muskegon Lake, this included oil spills. Several remediation projects in White Lake led to its delisting as an AOC in 2014. In contrast, remediation in Muskegon Lake is ongoing and this study focuses on the benefits of several projects that removed contaminated sediment and debris by 2018. Spatial Difference-in-Difference (DiD) approaches are used after Euclidean distance-based matching to estimate benefits, comparing housing price differences pre- and post-remediation between homes near the AOC (the treated group) and homes further away (the control group). Results reveal that without remediation homes near the White Lake AOC lose 12.4 percent of their value, with remediation mitigating losses to 4.5 percent. This implies a 64 percent recovery of housing values which amounts to $5.3 million in total benefits. Similarly, homes near the Muskegon Lake AOC lose 10.3 percent of their value with remediation reducing losses to 3.3 percent. This implies a 68 percent recovery of housing values and $11.2 million in total benefits. These findings reveal the property value benefits that can result from remediated AOCs.

Out of Sync Subnational Housing Markets and Macroprudential Policies in the UK

We examine whether regionally differentiated macroprudential policies can address financial stability concerns and moderate house price differences in the UK. We disaggregate both the household sector and the housing stock in a two-region DSGE model with out of sync subnational housing markets and compare four policy types: standard monetary policy, leaning against the wind monetary policy, national macroprudential policy or one that targets region-specific LTV ratios. In terms of reducing variances of house prices, regionally differentiated macroprudential policy performs best, provided the policy authorities are concerned with stabilising output and house prices rather than simply minimising the variance of inflation.

Understanding the Effects of Influential Factors on Housing Prices by Combining Extreme Gradient Boosting and a Hedonic Price Model (XGBoost-HPM)

The characteristics of housing and location conditions are the main drivers of spatial differences in housing prices, which is a topic attracting high interest in both real estate and geography research. One of the most popular models, the hedonic price model (HPM), has limitations in identifying nonlinear relationships and distinguishing the importance of influential factors. Therefore, extreme gradient boosting (XGBoost), a popular machine learning technology, and the HPM were combined to analyse the comprehensive effects of influential factors on housing prices. XGBoost was employed to identify the importance order of factors and HPM was adopted to reveal the value of the original non-market priced influential factors. The results showed that combining the two models can lead to good performance and increase understanding of the spatial variations in housing prices. Our work found that (1) the five most important variables for Shenzhen housing prices were distance to city centre, green view index, population density, property management fee and economic level; (2) space quality at the human scale had important effects on housing prices; and (3) some traditional factors, especially variables related to education, should be modified according to the development of the real estate market. The results showed that the demonstrated multisource geo-tagged data fusion framework, which integrated XGBoost and HPM, is practical and supports a comprehensive understanding of the relationships between housing prices and influential factors. The findings in this article provide essential implications for informing equitable housing policies and designing liveable neighbourhoods.

Analysis of house prices: a hedonic model proposal for Istanbul metropolitan area

The purpose of this paper is to explore the nature of housing price differences in Istanbul Metropolitan Area. Factors that affect house prices in Istanbul are investigated by Hedonic Regression Model with spatial variables. House Price taken in natural Log form as dependent variable and house characteristics, neighborhood characteristics and transportation infrastructure taken as independent variables. Location of houses on Europe continent has more impact on value than Asian part of the city. As the Istanbul city spread out new neighborhood areas are also developed with new characteristics. And those houses are sold according to amenities they surrounded by are spatially autocorrelated. House prices are examined in Istanbul by taking into consideration sub-market characteristics to identify localized factors. In the model distance to green areas, ports, proximity to metro stations, shopping malls, hospitals have positive but insignificant; proximity to earthquake fault lines, public bus transportation, industrial zones have negative and significant effect on prices.

Loss in the Time of Cholera: Long-Run Impact of a Disease Epidemic on the Urban Landscape

How do geographically concentrated income shocks influence the long-run spatial distribution of poverty within a city? We examine the impact on housing prices of a cholera epidemic in one neighborhood of nineteenth century London. Ten years after the epidemic, housing prices are significantly lower just inside the catchment area of the water pump that transmitted the disease. Moreover, differences in housing prices persist over the following 160 years. We make sense of these patterns by building a model of a rental market with frictions in which poor tenants exert a negative externality on their neighbors. This showcases how a locally concentrated income shock can persistently change the tenant composition of a block. (JEL D62, O18, R21, R31)

The Research on the Predicament of Further Promoting the Reform of Real Estate Tax

The price of real estate has been rising in recent years in our country, and the proportion of the consumption of urban households in housing investment gradually increases in the proportion of disposable income, which has affected the consumption of the urban residents. And at the same time, the real estate markets gradually become the markets of investment and hype. The high housing prices also contributed to the social gap between the rich and the poor. Therefore, the real estate tax reform is particularly important. However, since 2010, our country has put forward the reform of property tax, but the effect of the property tax reform practice is limited. So this article will discuss the existing problems in the process of further promoting the reform of real estate tax in China, such as the legislation of the reform of real estate tax, the coordination and consideration of the reform of real estate tax under the huge differences in housing prices, and the real estate information registration system in order to comb through to the existing problems, find the barriers and difficulties in further promoting the reform of the property tax, which has positive significance to further promoting the reform of the property tax.

Quantitative Estimation and Spatiotemporal Characteristic Analysis of Price Deviation in China's Housing Market

Differences in housing prices and rental prices across cities or regions and the relationship between prices and socioeconomic fundamentals are frequent research foci in urban and real estate economics, but the existing studies on China's housing market rarely consider both housing price and rent. This study provides a framework for a quantitative analysis of a country's housing market from the perspective of supply and demand, and takes China's housing market as a case study. The current study first explores the key factors that affect housing prices and rental prices using data from 202 cities and collected from 2011 to 2014. Then, theoretical values of housing prices and rental prices in each city are estimated, and the spatiotemporal characteristics of deviation of housing prices and rental prices are analysed. The empirical findings of the current study mainly reveal the following three points. First, the determinants of housing prices and rental prices have similarities. Second, the effect of above factors shows obvious spatial heterogeneity. The coefficients of the variables are different between coastal and inland regions, indicating different demand and supply elasticities across regions. Third, the price deviation presents significant spatial agglomeration. Cities with higher price deviations are clustered in the Yangtze River Delta and Beijing-Tianjin-Hebei, while the price deviations are relatively mild in the Pearl River Delta region.