House Prices Research Articles

The Utrecht Housing dataset: A housing appraisal dataset

This paper introduces a real-world dataset for analysing and predicting house prices. The dataset consists of actual data on the Dutch housing market collected in 2024 for a total of 153 houses in one city (Utrecht in The Netherlands). The dataset incorporates diverse variables on individual houses, includ- ing property characteristics (e.g., house type, build year, geolocation, area, energy label) and market metrics (e.g., asking price, final price). The data was collected from two public sources. The dataset has been created to help researchers and educators to demonstrate machine learning principles on several problem types. It can be used for classification (energy label and energy efficiency) and regression/ price estimation. There are ten original input features and one derived feature. The dataset can be freely used without restrictions under a Creative Commons license and is available via open data platform Kaggle.

Let’s Boost House Price Predictions: A Machine Learning Approach for Norwich

Let’s Boost House Price Predictions: A Machine Learning Approach for Norwich

A linear tessellation model for the identification of "food desert": A case study of Shanghai, China.

The "food desert" problem has been treated under a national strategy in the United States and other countries. At present, there is little research on the phenomenon of "food desert" in China. This study takes Shanghai as the research area and proposes a multiscale analysis method using a linear tessellation model that splits the street network into homogeneous linear units. Firstly, the network kernel density estimation using a linear tessellation model is used to measure the travel-mode-based food accessibility. Considering the actual travel constraints, the GPS trajectory data of four travel modes (walking, bicycle, metro and taxi) are applied to calculate the speed of each linear unit. Secondly, the "food desert" phenomenon in Shanghai are identified combing with the results of the network K-function. Finally, the resident income conditions in different modes are fitted based on the housing price data and the spatial distribution of four "food desert" patterns are detected by the overlay analysis of food accessibility and resident income conditions. The experimental results show that fifty percent of Shanghai is characterized by low food accessibility, and half of these areas are disadvantaged and low-income areas in suburbs, which are the locations experiencing the "food desert" phenomenon. Comparing the results of the proposed method and that of the traditional planar method, the identification results for all modes based on the traditional planar method underestimate the severity of the "food desert", especially for the bicycle and taxi modes. This study also provides corresponding decision-making reference for the alleviation and resolution of "food desert" issues. Moreover, the proposed method provides a new research perspective for urban research under the street network space.

Exploring the value of green: The impact factors on China's second-hand green housing prices based on geographically weighted Lasso regressions

Exploring the value of green: The impact factors on China's second-hand green housing prices based on geographically weighted Lasso regressions

Resilience Tested: Interplay between Mortgage Channels and Natural Disasters on Housing Price

The existing body of literature on the interaction between mortgage channels and natural disasters on housing prices is limited. As a vast archipelagic nation, Indonesia is highly susceptible to natural disasters. Our research utilizes quarterly data from Indonesian provinces between 2012 and 2019, and employs panel regression to evaluate the influence of mortgage channels and natural disasters on housing prices. The results indicate varying impacts on housing prices based on the type and intensity of natural disasters, alongside regional development, which remain robust under various tests. Despite potential disruptions, the overall availability and effects of housing loans on housing prices remain relatively stable, thus highlighting the significant role of mortgage channels in shaping housing prices under different circumstances. This understanding has the potential to guide stakeholders in enhancing disaster management strategies and overseeing housing prices, and takes into consideration the distinct vulnerabilities found in each region. Due to the unpredictable nature of natural calamities, it is crucial to establish plans for pre-disaster strategies. Using risk-disaster clusters allows the government to customize housing policies based on particular vulnerability categorizations instead of utilizing a standardized approach nationwide. Additionally, the financial sector has the opportunity to integrate regional risk assessments in the decision-making process for the distribution of housing loans.

Equity Release Mortgages in the UK: Regional Characteristics of Demand and Supply

We consider the factors that affect regional variations in equity release mortgages (ERMs) in the United Kingdom (UK). These are a growing section of the market, but with regional differences in factors that affect demand and supply. We estimate a time series cross-sectional (TSCS) model within a simultaneous equation framework to analyse the number of ERMs (a proxy for the demand for ER debt) and the average loan-to-value (LTV) ratios, a proxy for supplied loan terms. We include variables that cover demographics, demand, pricing, risk, macroeconomic factors and regional dummies. For the number of ER loans, we find positive effects from net pension income and holiday costs, and negative effects from the average LTV ratio levels, state benefits, first-time buyer interest rates, female life expectancy, mortgage price differential, house price growth and gross value added (GVA). For the average LTV ratios, there are positive effects from gross pension income, average borrower age, holiday costs, and the risk premium, as well as negative effects from the number of ERM loans, female life expectancy, and the level of real house prices. Overall, households who are living in southern, wealthier areas have greater access to, and take greater advantage of, ERM loans but require lower LTV ratios. Our key findings are generalisable across the many countries that offer similar mortgage products and so our analysis offers insights for borrowers, lenders, and regulators.

Ageing, Credit and House Prices: Comparison between OECD and Non-OECD Countries

Ageing, Credit and House Prices: Comparison between OECD and Non-OECD Countries

주택가격 변동의 구조적 요인에 관한 연구 - 시계열 및 분기점 분석을 중심으로

The results of the autoregressive time series analysis (ARIMAX) indicate that housing supply-demand balance was a significant variable in the Seoul metropolitan area, while autoregressive terms and jeonse prices were identified as key factors in non-metropolitan regions. In the breakpoint regression analysis (BREAKLS), during the downturn period, sale prices and jeonse prices were significant in both metropolitan and non-metropolitan areas. During the upturn period, supply-demand balance exhibited a strong influence in the metropolitan area, while supply-demand balance and money supply were significant in non-metropolitan areas. Notably, in the highly volatile metropolitan area, supply-demand balance was confirmed as a critical determinant of housing prices during the upturn period.

시차분포모형을 이용한 인구변화와 주택가격의 인과성 분석

This study analyzed the causality between population changes and housing price fluctuations by applying a distributed lag model and Granger causality test. First, the causality between housing sale prices and the three demographic factors was analyzed. The analysis results revealed that marriage and housing sale prices mutually influence each other, while housing sale prices affect mortality, and birth rates are influenced by housing sale prices. Next, the causality between housing lease prices (jeonse) and the three demographic factors was analyzed. The results showed no bidirectional Granger causality between housing lease prices and any of the three demographic factors. This indicates that birth rates, mortality, and marriage do not have mutual influences with housing lease prices. The findings of this study revealed that changes in housing sale prices have causality with demographic changes, whereas housing lease prices (jeonse) do not exhibit such causality. Therefore, a key policy implication is that demographic changes must be closely analyzed and addressed to ensure the stability of the real estate market in the future.

Affordable housing solutions: real estate strategies for addressing urban population growth

Introduction: As the population count in cities increases, the need for affordable and efficient housing systems becomes even more pressing, especially for the poor and middle-income earners. Housing affordability challenges are an important area to which real estate strategies contribute, and this includes funding schemes, resource-efficient construction methods, and a repurposing of structures for different functions. These strategies work towards achieving the goal of closing the deficit of housing availability in the market as well as meeting the necessary criteria for sustainability and habitability of cities of the future. Materials and Methods: The research methodology employed a comprehensive literature review, analysing peer-reviewed articles, industry reports, and case studies from diverse urban contexts. The data collection was majorly centered on successful affordable housing projects, policies, and approaches used in various city settings. Both quantitative and qualitative assessments were used in the studies for comparative analysis of different strategies and solutions with respect to their efficiency and applicability with regard to cost-saving, mass implementation, and social aspects. Finally, a focus on the world of urban planning and real estate with interviews with professionals in these fields as well as policymakers gave an understanding of possible actual difficulties and opportunities of realizing affordable housing strategies. Results: The study revealed several key findings regarding effective real estate strategies for affordable housing. Financing models that were discussed in this paper include public-private partnerships and community land trusts showed promising results regarding the possibility of decreasing housing prices and making housing more accessible. Techno-friendly mechanisms such as modular construction accompanied by the use of lightweight building materials were perceived to be helpful in increasing the rate of construction of houses while reducing the effects of the construction activities on the physical environment. Adaptive reuse mortels demonstrated that movement’s ability to re purpose inactive urban areas as viable affordable housing opportunities satisfied both the demand for housing and site redevelopment. In addition, policy interventions like inclusionary zoning and density bonus were useful in enhancing the private sector’s engagement in affordable housing. Discussion: The research findings highlight the multifaceted nature of affordable housing solutions and the importance of context-specific strategies. Some strategies like public-private partnerships, could be adopted in any city regardless of the type, however the decision on their application depended on the local socio-economic situation and legislation. Effective implementation of sustainable construction depended on appropriate skills by human resources and favourable policies. Architects and developers of adaptive reuse projects encountered issues with code conformity and reception among the population. These findings suggest that the solution to affordable housing must employ the right mix of innovative real estate solutions, supportive public policies, and active community participation. Conclusion: Real estate strategies offer promising avenues for addressing the affordable housing crisis in rapidly growing urban areas. Innovative financing strategies; efficient construction practices; as well as adaptive reuse strategies provide an opportunity for extending the amount of affordable housing in cities alongside sustainable development. However, the accomplishment of these strategies entails cooperation from public, private, and the community sectors

PENENTUAN HARGA JUAL PRODUK DENGAN PENDEKATAN MODEL HEDONIK: KASUS DI KOTA BANJARMASIN DAN KOTA SURABAYA

This research aims to know at the factors that determine the selling price of houses in Banjarmasin City and Surabaya City. The Hedonic Price Method is used to evaluate environmental services, where the presence of environmental services directly influences the selling price. The selling price can be influenced by land area, building area, number of space (bedrooms and bathrooms), certificate of ownership, quality of building materials, security and access to public facilities. Research approach is used with quantitative statistics. Housing sales data of 50 samples were analyzed for each city obtained randomly. Total sample safety with 100 observations. Regression method with Principal Component Analysis (PCA) is used to test the research hypothesis and is processed with Minitab software version 19. Based on the results of the analysis, the factors that influence the selling price of houses in the city of Surabaya are land area, building area, number of rooms and number of bathrooms. While the factors that determine the selling price in the city of Banjarmasin are building area, certificates, quality of building materials and building facilities.

Unlocking code to livability: indemnificatory housing supply, fiscal decentralization, and housing prices in China

ABSTRACT By developing a multi-sector general equilibrium model, this article investigates the nonlinear relationship between indemnificatory housing supply (IHS) and housing prices in China. Utilizing panel data from 31 provinces from 2009 to 2019, we find that the overall effect of IHS on housing prices is negative at the current stage. However, as the IHS expands, this effect transitions from negative to positive, exhibiting a U-shaped relationship driven by a stable demand-shifting effect and an increasingly dominant supply-crowding-out effect. Furthermore, fiscal decentralization exhibits a nonlinear moderating effect, first strengthening and then weakening the effect of IHS on lowering housing prices. Our findings demonstrate the relationship between IHS and housing prices from a nonlinear perspective, highlight the critical role of fiscal decentralization, and provide policy insights for promoting economic growth and improving social welfare.

The Research on Factors Influencing House Price-Taking Perth as an Example

This article aims to identify the factors that have an impact on house prices. Using the method of Multiple Linear Regression to analyze the important factors with samples of Perth. Based on assumption, 8 variables were chosen to find the correlation of the price of house in the suburbs of Perth. This article also considers the interaction effects between the area of the parts of the house which contains the area of the land, the area of the room and number of some types of rooms includes garage, bedroom, living room and the date the house was sold, the distance to nearest public facilities such as station and school and the distance from the house to the central business district and the rank of the nearest school. In order to test the effectiveness of the operation, the research consider the significance of those variables, it turns out the longitude and latitude of the house and the year of building and selling and the postcode of the house. Overall, the fluctuation of the price of house in Perth can be considered by the extent to which these factors affect them.

Examining the Impacts of House Prices on Self-Rated Health of Older Adults: The Mediating Role of Subjective Well-Being

As the global aging trend increases, older adults are placing greater emphasis on their health. Evidence indicates that there is a complex association between house prices and older adults’ health, with their subjective well-being potentially acting as a mediator in this connection. A mediation model, utilizing data from China’s 2018 Labor Dynamics Survey, was employed to examine the impact pathway of house prices, subjective well-being, and self-rated health, while investigating the differences between young-old and old-old groups. The major findings are as follows: (1) House prices negatively affected self-rated health among the older adults. (2) The subjective well-being of older adults mediated the pathway through which house prices affected their self-rated health. (3) For old-old adults, higher house prices were more strongly linked to an increased likelihood of reporting good, very good, or excellent health. Subjective well-being was more significantly associated with reporting better health among the young-old group. Compared with the young-old population, the impact of house prices on self-rated health was stronger among the old-old, and the degree increased with increasing age. Consequently, to improve older adults’ well-being and self-rated health, effective healthy-aging policies should not only consider the influence of the real estate market, but also balance the allocation of elderly service facilities, promote affordable housing, and implement a combination of medical and nursing care from the perspective of urban planning.

The Effect of Housing Prices on Urban Innovation Capability: New Evidence From 246 Chinese Cities

ABSTRACTThis study investigates the impact of rising housing prices on urban innovation capabilities from a comprehensive and systemic perspective, using panel data from 246 Chinese cities from 2004 to 2020. The findings challenge traditional views that emphasize only the negative effects of high housing prices on innovation. Instead, the analysis reveals that rising housing prices can enhance urban innovation through specific mechanisms. These include attracting and concentrating talent and generating spatial spillover effects that benefit neighboring cities. The study employs feasible methodological approaches, including the analytic hierarchy process (AHP), to measure urban innovation capability and spatial econometric models to capture spillover effects. The results provide valuable insights for policymakers to promote urban innovation, including stabilizing housing markets, optimizing industrial structures, attracting high‐skilled individuals, and establishing inter‐city innovation coordination mechanisms. This research contributes a novel theoretical framework for understanding the complex relationship between housing prices and urban innovation, enriching the fields of urban economics and innovation studies.

Tests for spatial dependence and temporal heterogeneity in time-varying coefficient spatial autoregressive panel data model

Time-varying coefficient spatial autoregressive panel data model is a powerful tool to simultaneously deal with spatial dependence and temporal heterogeneity in spatial panel data analysis. However, little work has been devoted to related statistical inference issues, which largely limits the application scope of the model. In this paper, we develop two generalized-likelihood-ratio-statistic-based bootstrap tests to detect spatial dependence of the response variable and temporal heterogeneity of the regression relationship, respectively. The simulation studies show that both tests are of accurate size and satisfactory power, and are quite robust to non-normality of error distribution. A house price data set is finally analyzed to demonstrate the application of the proposed tests in detecting spatial dependence and temporal heterogeneity.

Forecasting House Prices in Shanghai Using Common Factor Analysis and Multiple Linear Regression Analysis

Predicting housing prices is critical for understanding economic trends and making decisions in real estate markets. This is accomplished by identifying important influencers and utilizing prediction models. Predicting these prices is essential for understanding broader economic trends and guiding decisions in real estate markets. The model was trained and evaluated using economic and financial data from the CEInet Statistics Database, with key variables representing major financial, demographic, and economic characteristics included. To estimate housing prices, a combination method combining common factor analysis and multiple linear regression was used. The KMO test result of 0.845 and the scores of Bartlett's Test of Sphericity (2=4296.29, p=0.000) confirmed the data's suitability for factor analysis. Factor analysis reduced the dimension of the dataset, revealing two important components responsible for 80.01% of the volatility in home costs. The multiple linear regression model based on these characteristics was tuned to attain good prediction accuracy. The model predicted housing prices in Shanghai with an accuracy of 94.54%.

Research on the Influencing Factors of House Prices in Sydney

Considering various factors that affect house prices, this study investigates whether housing prices in Sydney can maintain their value or even increase in the future. A multiple linear regression model was applied using a dataset obtained from Kaggle, which includes 11,161 real estate transactions in Sydney from 2016 to 2021. This paper uses it to explore the relationship between housing prices and 12 independent variables (originally 14 but some have to be abandoned), such as the number of bedrooms, property size, median suburban income, and distance to the central business district (CBD). The results indicate that some factors have a positive, negative, or no impact on house prices and that 39.6% of the real estate price is influenced by them, indicating a moderate level of predictability. These findings provide valuable insights for investors and policymakers interested in the dynamics of the Sydney real estate market, emphasizing the importance of geographic location and property attributes in determining housing prices.

The Research on Factors Influencing Housing Prices -Take Kansas City as an Example

This article aims to identify those factors that have an impact in housing prices. The sample of the study is 15 variables, including 19554 pieces of data. Among the 15 variables, there are both factors of the house itself and some external influencing factors. The method of Multiple Linear Regression is used to analyze the significant factors with the housing prices of Kansas City. Based on these 15 variables it does correlate with house prices. The 19554 data substituted into Multiple Linear Regression Model. The result shows that grade, latitude, years of built, waterfront, view, square of basement, square of above, zip code, bathrooms and condition have a significant linear relationship with prices, while the id, date, bedrooms, square of lot, floors and year of renovating fail the significance test. In totally, the volatility of housing prices in Kansas City can be considered by the extent to which these factors affect them.

The Research on Factors Influencing Housing Prices-Taking California for Example

This article is about something on impacting factors of house prices. In this research, multiple linear regression is used as a method. Regression method is a scientific tool that provides insights into the dynamics of how a dependent X factor responds to changes in one or more independent variables. This research finds that there are many factors and Indies do influence on the housing prices, including GDP, CPI, Income, Urban population, Delinquency rate, etc. Most of those factors or Indies have positive correlation with housing price. And through further analysis selection and analysis, research find that CPI and GDP has positive relation vs housing price and its correlation degree is most highly in those factors. And then GDP and CPI working as independent variables in multi linear regression come to a model. For this model, T test, R-squared test, F test and practical test have been done to proof this model is reasonable. And this model can be used in practical housing price analysis.