House Prices Research Articles

Industrial robot applications and individual migration decision: evidence from households in China

The integration of industrial robot application (IRA) into various sectors has catalyzed significant transformations in the labor market, reshaping the dynamics of individual migration decision (IMD). As industries increasingly adopt automation, the implications for workforce distribution and migration patterns become critical areas of study. Based on the conditional logit model and using CMDS data, this paper explores the relationship between IRA and IMD, as well as the heterogeneity effect and influencing mechanism. The findings indicate that IRA reduces the probability of the floating population choosing the city, and the results are robust after considering endogeneity and further robustness test. The impact of IRA on IMD is heterogeneous. Relatively speaking, the floating population with high education levels, health, younger, unmarried, female, agricultural household registration, state-owned enterprises, and non-routine task work does not over-think the impact of IRA during the migration process. In addition, the floating population also considers geographical location, city size, marketization level, wage level, and environmental quality when making migration decisions. the mechanism test shows that IRA attracts or suppresses the floating population through factors such as wage levels, housing price level, difficulty in finding a job, etc., depending on the result of individual utility maximization. These insights reveal the complex interplay between technological advancements in robotics and labor migration, emphasizing the need for comprehensive policy frameworks to manage these transformations effectively.

The Impact of Property-Purchasing Limitations Policy on Housing Price: Evidence from Xiamen and Shantou

The Impact of Property-Purchasing Limitations Policy on Housing Price: Evidence from Xiamen and Shantou

A Study on the Impact of High-Quality Education Resources on Urban Housing Prices: An Empirical Study Based on the DID Model

A Study on the Impact of High-Quality Education Resources on Urban Housing Prices: An Empirical Study Based on the DID Model

Impact of the Russia-Ukraine Conflict on Russian House Prices

Impact of the Russia-Ukraine Conflict on Russian House Prices

Explainable Graph Neural Networks: An Application to Open Statistics Knowledge Graphs for Estimating House Prices

In the rapidly evolving field of real estate economics, the prediction of house prices continues to be a complex challenge, intricately tied to a multitude of socio-economic factors. Traditional predictive models often overlook spatial interdependencies that significantly influence housing prices. The objective of this study is to leverage Graph Neural Networks (GNNs) on open statistics knowledge graphs to model these spatial dependencies and predict house prices across Scotland’s 2011 data zones. The methodology involves retrieving integrated statistical indicators from the official Scottish Open Government Data portal and applying three representative GNN algorithms: ChebNet, GCN, and GraphSAGE. These GNNs are compared against traditional models, including the tabular-based XGBoost and a simple Multi-Layer Perceptron (MLP), demonstrating superior prediction accuracy. Innovative contributions of this study include the use of GNNs to model spatial dependencies in real estate economics and the application of local and global explainability techniques to enhance transparency and trust in the predictions. The global feature importance is determined by a logistic regression surrogate model while the local, region-level understanding of the GNN predictions is achieved through the use of GNNExplainer. Explainability results are compared with those from a previous work that applied the XGBoost machine learning algorithm and the SHapley Additive exPlanations (SHAP) explainability framework on the same dataset. Interestingly, both the global surrogate model and the SHAP approach underscored the comparative illness factor, a health indicator, and the ratio of detached dwellings as the most crucial features in the global explainability. In the case of local explanations, while both methods showed similar results, the GNN approach provided a richer, more comprehensive understanding of the predictions for two specific data zones.

Prediction of new housing prices in Changsha urban area based on multiple machine learning algorithms: A comparative analysis

<p>As China’s pillar industry, the property market has suffered a considerable impact in recent years, with a decline in turnover and many developers at risk of bankruptcy. As one of the most concerned factors for stakeholders, housing prices need to be predicted more objectively and accurately to minimize decision-making errors by developers and consumers. Many prediction models in recent years have been unfriendly to consumers due to technical difficulties, high data demand, and varying factors affecting house prices in different regions. A uniform model across the country cannot capture local differences accurately, so this study compares and analyses the fitting effects of multiple machine learning models using February 2024 new building data in Changsha as an example, aiming to provide consumers with a simple and practical reference for prediction methods. The modeling exploration applies several regression techniques based on machine learning algorithms, such as Stepwise regression, Robust regression, Lasso regression, Ridge regression, Ordinary Least Squares (OLS) regression, Extreme Gradient Boosted regression (XGBoost), and Random Forest (RF) regression. These algorithms are used to construct forecasting models, and the best-performing model is selected by conducting a comparative analysis of the forecasting errors obtained between these models. The research found that machine learning is a practical approach to property price prediction, with least squares regression and Lasso regression providing relatively more convincing results.</p>

A stochastic loss given default model of Hungarian residential mortgages

AbstractThe paper analyses the Loss Given Default (LGD) rates of residential mortgages, using a model based on stochastic collateral value. The implementation of the model is based on exponential Ornstein-Uhlenbeck processes fitted to the Hungarian regions' house price indices. According to the model results, in case of a mortgage with a 80% loan-to-value ratio at origination, the expected LGD is around 30–40%, depending on the region. The highest LGD rates are estimated for villages, while the lowest rates are expected in Budapest and cities in the middle of the country. The range of the regional differences can reach 7 percentage points. According to the LGD Risk index based on the aggregated model, the LGD risk profile of recently issued mortgages has improved significantly since 2009 in Hungary.Due to the strong negative relation between the house prices and mortgage default rates, the expected return on defaulted collateral value tends to be low. The results could be relevant for credit institutions in their mortgage origination decisions and enhance analysis of lending processes and the associated risks.

Structural Attributes and Prices of Warehouses in Uyo Urban, Akwa Ibom State, Nigeria

Warehouses have now taken on a greater significance in the development of the economy of any nation. The aim of this study was to evaluate the effects of structural attributes on prices of warehouses in Uyo Urban, Akwa Ibom State. In order to attain the stated aim, the following objectives were set; to assess the structural attributes of warehouses in the study area; to examine the average prices of sampled warehouses across the study area and to establish the relationship between the structural attributes and prices of warehouses in the study area. The research employed the survey design. The independent variables were the structural attributes measured using room size, parking space and state of repairs while the dependent variable was measured using the price of ware houses. Both descriptive and inferential statistics were employed in the analysis of the data, with the help of the SPSS and Microsoft Excel software. The research revealed the prices of 48 selected warehouses in the study area and low prices were noticed in properties with poor structural attributes and vice versa. The study revealed a 99.75% significant relationship between the dependent and the independent variables, with a Durbin-Watson statistic of 1.979 and F-ratio of 5908.123. The study concluded that there was a statistically significant relationship between structural attributes and prices of warehouses in Uyo Urban, Akwa Ibom State. However, the study recommended that investors in industrial properties including warehouses should put structural attributes into consideration as poor structural attributes were noticed to bring about poor returns in prices.

China’s GDP-at-Risk: Real-Time Monitoring, Risk Tracing, and Macroeconomic Policy Effects

Timely monitoring GDP-at-risk and tracing economic downside risk sources can help establish effective risk warning and prevention systems. This study constructs a probability distribution for China’s economic growth with skewness determined by a multidimensional predictor information set of macro fundamentals. Such a treatment allows us to identify changing drivers of economic downside risks during monitoring GDP-at-risk’s dynamic evolutionary path. We also employ a time-varying parameter vector autoregression model with random volatility to explore the heterogeneous impacts of different macroeconomic policy instruments on economic slowdowns. Our results provide empirical support for macroeconomic management and policy formulation in emerging markets. We reach three conclusions. First, the dynamics of GDP-at-risk exhibit significant event-driven characteristics, and economic downside risk increases significantly under the influence of extreme events. Moreover, the probability distribution of economic growth is asymmetric--as the downside risk of the economy increases, its upside potential increases disproportionately. Second, the time-varying risk trace of GDP-at-risk shows that the contribution of financial conditions and local government debt to economic downside risk declines. The importance of the risk-driving role of housing price growth gradually increases, suggesting that China’s property prices can provide more valuable early warning information about future growth risk, allowing time for precise preventive measures. Nevertheless, interest rates and inflation as risk divers have consistently minimal impacts. Third, the heterogeneity impulse response function of GDP-at-risk suggests that quantity-based monetary policy and fiscal policy can manage economic downside risks in the short run. In contrast, price-based monetary policy can curb economic overheating and reduce downside risks in the medium to long term. Therefore, the effect of price-based monetary policy is more sustainable in China.

Macrofinancial linkages and income distribution in the South African economy

ABSTRACT This study examines the impact of financial factors and income distribution on aggregate demand and growth in South Africa, using data spanning 1980–2019. We applied the autoregressive-distributed-lag bounds-testing procedure to uncover the effects over the short-and-long-run horizons. Additionally, we estimated impulse response functions to reveal further dynamic interactions. Our findings indicate that the key financial factors affecting the South African real economy are credit growth, interest rates, house prices and exchange rates. House prices play an important role, via the collateral channel, in driving consumption demand. Meanwhile, credit growth stifles investment demand in the long run. Interest rates constrain demand and growth, while the strengthening of exchange rates improves investment for firms. However, exchange rates impact negatively on GDP, presumably, through the trade-channel. We also found that income inequality suppresses consumption and investment demand, as well as GDP. This suppression appeared to be relatively pronounced in the short run for investment and growth, while for consumption, the suppressive impact was persistent. Therefore, we recommend that any policy framework aiming to galvanise growth should not overlook the problem of inequality. Otherwise, any headway made on growth will be back-pedalled by the demand-reducing impact of high-income inequality.

Analysis of the Influence of Domestic Products, Loan Interest Rates, and Residential Property Price Index on Home Ownership Credit Distribution in Indonesia for the Period 2015:TW.I – 2023:TW.III

This research examines the relationship between Gross Domestic Product (GDP), interest rates and house prices on mortgages in Indonesia. Using the dynamic ECM Domowitz-Elbadawi method, this research explores the short and long term relationships between these variables. House prices are measured using the composite Residential Property Price Index in the primary market. The research results show that GDP has a positive and significant effect on mortgage distribution in the short term. On the other hand, in the long term, both interest rates and house prices have a negative and significant influence on mortgage distribution. These findings are valuable for strategic decision making in property investment and policy formulation, suggesting that increasing GDP can boost mortgage distribution, while managing interest rates and house prices is important for long-term stability.

Machine learning for real estate valuation: Astana, Kazakhstan case

Purpose of this research is to investigate the accuracy of machine learning models in forecasting and evaluating house prices, and to understand the key factors that impact pricing. The study involved analyzing data scraped from real estate ads in the “sale of secondary housing” category on the website krisha.kz. The paper emphasizes the importance of understanding the factors that affect house prices, such as quality, location, size, and building materials. It was concluded that these factors have a strong correlation with house price prediction. The information available on krisha.kz was found to be a useful resource for finding good apartments. The data collected by the scraper was analyzed by models: Linear regression (LR), interactions linear regression (ILR), robust linear regression (RLR), fine tree regression (FTR), medium tree regression (MTR), coarse tree regression (CTR), linear support vector machine (LSVM), quadratic SVM (QSVM), medium gaussian SVM (MGSVM), rational quadratic gaussian process regression (RQGPR), boosted trees (BoosT), bagged trees (BagT), neural network based on the bayesian regularization algorithm (BR-BPNN). BR-BPNN showed better results than other models, with an MSE of 32.14 and R of 0.9899.

Do 5G cell phone towers decrease house prices? Evidence from Warsaw

AbstractThis study investigates the impact of telecommunication infrastructure on residential property prices in Poland. This study contributes to the discussion of economic externalities related to new public infrastructure within the urban landscape. We use hedonic regression, matching techniques, and a difference-in-differences estimator to assess the impact of base transceiver stations on apartment prices in Warsaw, the capital of Poland, which covered market data on the sales of residential premises located in Miasteczko Wilanów estate in Warsaw from 2016 to 2021. In the analysed period, 1,825 residential sales were recorded and used for econometric modelling. The results do not confirm the influence of the vicinity of the wireless communication technology infrastructure on residential real estate prices in the studied local market. In particular, we did not observe a detrimental effect of cell phone towers on housing prices. Additionally, we investigate whether residential sale prices in proximity to BTS changed significantly after the introduction of the 5G standard. This particular issue has not been addressed in the economic literature. We found that the sale prices of apartments located in Warsaw were not statistically affected by the introduction of a fifth-generation technology standard for broadband cellular networks. Our research contributes to a better understanding of stigmatisation effects related to telecommunication infrastructure, and in particular, the links between the presence of cell phone towers and residential property values in the neighbourhood. The results may be of interest to all potential agents involved in neighbourhood conflicts arising from investments in cell phone towers and the development of new communication infrastructure in urban landscapes.

Application of the ARIMA model in house price index in Malaysia

The factors that affecting the escalating price of houses in Malaysia are driven by factors such as population growth, income dynamics, interest rates, and GDP. This phenomenon has notably outpaced the growth of household incomes, thus majorly impacting Malaysians. The study’s primary goal is to forecast the housing price index in Malaysia from the best model obtained using Box-Jenkins method. aligning with the 2018-2025 National Housing Policy objectives, utilizing advanced machine learning and time series modeling. The objectives guide the research: to find the best model for predicting house price index in Malaysia using Box-Jenkins Method. Utilizing secondary data from the National Property Information Centre (NAPIC) spanning from 1988 to 2023, the study employed the analytical method of ARIMA. The results favored the ARIMA (1,1,1) model as the best model in predicting housing price indexes. This offers an excellent forecasting model for residential properties towards gaining better understanding of their pricing dynamics and offers potential solutions to the issue of housing affordability for Malaysians.

Dynamics of urban sprawl: Deciphering the role of land prices and transportation costs in government-led urbanization

While the relationship among land price, transportation cost, and urban sprawl under the condition of local governments monopolizing land supply has been rarely discussed in the literature, this paper addresses this gap by hypothesizing based on the monocentric city model. The hypothesis suggests that due to increasing land prices and decreasing transportation costs, urban fringes are more likely to be developed, leading residents to move away from the city center and altering the demographic pattern of urban sprawl. An empirical analysis is conducted using panel data from 104 cities in China, including remote sensing data of urban land construction areas. The results show that high land prices and low transportation costs drive urban sprawl, with varying impacts across cities. The coupling coordination analysis indicates increasing influence of urban sprawl on land prices and transportation costs. These findings suggest the government should address the effects of unplanned urban expansion on these factors. The findings suggest that the government should pay more attention to the effects of unplanned urban expansion on land prices and transportation costs. Understanding the role of local governments in urban planning is crucial for comprehending China's urbanization strategy and its implications on social exclusion, gentrification, high housing prices, and other urban issues.

Investigation of Influential Factors of Housing Price

The fluctuation of housing prices and its influencing factors are important research topics in economics. Under the current background of rapid globalization and urbanization, understanding the driving factors of housing prices is not only an important contribution to academic research but also has important guiding significance for policymakers and individual home buyers. This paper explores the key factors affecting housing prices, focusing on the impact of variables such as living space, land space, housing landscape, housing conditions, housing quality, and housing construction year on housing prices. This study uses multiple regression analysis to analyze the US housing price data set. The results show that living space, housing landscape, housing quality, and housing construction year have statistically significant effects on housing prices at a significance level of 1%. This study provides an effective reference to real estate developers, policymakers, and individual home buyers, which can help them understand market demand more deeply, make decisions that are more in line with market trends, and promote the dynamic and functional development of the real estate market.

Housing prices, buses and trams in Medellín (Colombia)

This paper aims to establish the impact of two medium-capacity transportation systems (MCTS) on housing prices in Medellín (Colombia): Metroplús, a bus rapid transit (BRT) system, and Tranvía, a tramway system. Using repeated cross-sectional data from the Medellín Quality of Life Survey from 2008 to 2018 and difference-in-differences estimators, we find that Metroplús has a negative impact on the growth of rental prices, whereas Tranvía has a positive impact. We do not find any effect on several other outcomes, such as the perception of quality and coverage of the public transportation in the neighborhoods they serve, and the number of private vehicles in the household.

HOUSING PRICES AS PAYMENT FOR ECOSYSTEM SERVICES OF URBAN GREEN SPACES: CASE STUDY IN KYIV

The purpose of the study is to analyze the impact of the availability of urban green spaces (UGS) and air quality on the cost of residential real estate in Kyiv. Additionally, it seeks to identify the level of awareness among city residents regarding the advantages of living close to UGSs in areas with relatively clean air and their willingness to pay a higher price for such housing. The QGIS software was employed to assess the pedestrian and time accessibility levels of recreational green spaces, and the results are depicted on the corresponding map. The least accessible for everyday recreation is the UGS in the central districts of the city, which traditionally have the highest population density and the most expensive residential real estate. The article analyzes the priority criteria for determining the housing cost, depending on the spatial localization relative to the city center and the comfort class of real estate. The distribution of housing across different tiers and the price range for the apartments in new constructions in Kyiv during 2015–2021 were also examined. To identify the correlation between the cost of residential real estate in the capital’s new buildings and the availability of green spaces nearby, we investigated the spatial distribution of prices in the largest residential complexes in the city. The results of spatial visualization obtained through Voronoi diagrams or Delaunay triangulation indicate significant disparities in the cost of housing located in the center of Kyiv or on the periphery. Logically, residential complexes with the highest apartment prices are located in the central districts of Kyiv and near the metro stations. However, most of these locations lack accessible green areas suitable for everyday recreation. Therefore, there is no positive correlation between housing prices and the presence of UGSs nearby. To achieve the research goal, we also conducted a sociological survey among Kyiv residents in various districts of the city. The survey results indicate that most Kyiv residents do not have the desire or are not currently willing to pay a higher price for the opportunity to live near a park or forest and breathe clean air, i.e., to enjoy the ecosystem services provided by UGSs fully. The scientific novelty of the research lies in determining the pedestrian and time accessibility of recreational green spaces in Kyiv, identifying trends in spatial clustering of the most expensive residential real estate in central city areas, and demonstrating the lack of a positive impact of the availability of green areas on the per square meter cost of housing in new developments. Additionally, the study reveals a remarkably low willingness of the city residents to pay a higher price for the opportunity to receive ecosystem services from available UGSs.

Spatial valuation of urban green lungs: Unveiling the true worth of urban parks through MGWR in Chengdu, China

Urban green spaces (UGS), often metaphorically referred to as the “green lungs of cities,” play a crucial role in sustainable urban development and are a key element of land-use policy. This study evaluates the economic value conferred by UGS through urban parks, employing a Multi-scale Geographically Weighted Regression (MGWR) model within a hedonic pricing framework. By focusing on the localized impact of UGS on housing markets in developing megacities, this research utilizes Chengdu, China, as a case study to reveal spatial patterns in property valuations. The findings demonstrate a significant localized impact of UGS on housing prices, characterized by pronounced spatial heterogeneity. A substantial premium on housing values is correlated with proximity to urban parks, reflecting a high market valuation of these green spaces. This premium varies spatially, diminishing in more peripheral urban zones but still significantly influencing overall housing market dynamics. The study highlights the importance of reassessing rapid, policy-driven park expansions. There is a need for value-based UGS integration in sustainable urban planning, emphasizing strategic, ecologically informed decision-making that balances land development with conservation and meaningful resident engagement.

Migration across the urban hierarchy: Has China’s urbanisation transitioned from the primate city stage to the secondary city stage?

The population density in megacities in China gives rise to challenges, such as traffic congestion and soaring housing prices. A trend of leaving primate cities can be observed as well as a population increase in secondary cities. These trends might point to an urbanisation transition from the primate city stage to the secondary city stage. Research is needed to determine at which stage of urbanisation China currently resides, and who are migrating across the different levels of cities in this stage. In order to answer these questions, the current study combines the theory of differential urbanisation and migrant selectivity, and analyses city-level migration patterns and demographic characteristics of migrants across the urban hierarchy. The findings indicate that China is currently in the intermediate primate city stage, where the upward migration across the urban hierarchy is driven by younger adults with higher education and income, and a lower likelihood of marriage or parenthood. Building upon global evidence, this research further extends the theory of differential urbanisation by incorporating migrant selectivity into the interpretation of urbanisation stages. It reveals that educated migrants tend to concentrate and move up the urban hierarchy in the primate city stage but might deconcentrate during the secondary and small city stages. This study offers practical insights for policymakers at the national and city levels to develop population growth plans, adjust targeted migration policies and respond to future urbanisation processes.