House Prices Research Articles

Unveiling China's Fertility Dilemma: Analyzing Risk Factors and Their Global Implications

Abstract: The National Bureau of Statistics of China has announced in 2021 that the country has formally entered the stage of low fertility rate, reaching a historically low of 1.3. Such low birth is far below the replacement level of 2.1 which is needed for maintaining a sustainable population, implying the possibility for China to start a significant demographic shift. Several key factors have contributed to this decline. Factors behind the problem includes Chinas historical policy implementation, socioeconomic factors including the rise of housing price and cost of upbringing and social factors including waning of social norms and ascent of gender equity. Such phenomenon serves as also valuable insights for analyzing global fertility intention for countries like Switzerland, the United States and South Korea. As all global policymakers seek to address the problem of unstable and declining fertility rates and waning fertility intention, the Chinese case could provide intriguing example as supportive evidence.

Can Social Media Information Amplify Short-term Housing Price Changes? An Investigation in China’s Major Cities

Can Social Media Information Amplify Short-term Housing Price Changes? An Investigation in China’s Major Cities

Leave or stay? The determinants of the migration behavior of university graduates in Shanghai

ABSTRACT Millions of university graduates in China either stay in the cities where they go to universities (for their bachelor’s degree) or migrate to other areas each year. What factors determine their destination choice? This paper investigates migration patterns of university graduates from Shanghai among the university city, hometown, and other cities, and the roles of general factors and special Chinese factors such as hukou in graduate mobility. The main findings are as follows. First, key factors that attract graduates to stay in the university city, return to their hometown, or go to other cities are different and one factor may not have the same impact. Second, there are specific spatial patterns of migration from different types of hometowns to three destination types, as different types of hometowns offer different life experiences as well as opportunities in the spatial administrative system. There is a strong preference for Chinese graduates to move up the urban hierarchy to bigger cities than their hometowns. Third, the impacts of some general factors such as housing price, social capital, and human capital are similar to those of Western countries. The impacts of hukou vary. Although graduates have local hukou in their hometowns, most graduates from big cities, towns, and villages prefer not to return to their hometowns. Only graduates from small and medium-sized cities consider returning to their hometowns. But if graduates go to other cities, they do consider hukou factor. Our research has significant implications for urban and regional development and talent policies in China.

House Price Pridiction Using Machine Learning

This project provides us an overview on how to predict house prices using various machine learning models with the help of different python libraries. This proposed model considers as the most accurate model used for calculating the house price and provides a most accurate prediction. This provides a brief introduction which will be needed to predict the house price. This project consists of what and how the house price model works with the assistance of machine learning technique using scikit-learn and which datasets we will be using in our proposed model. Predicting the price of a house helps for determine the selling price of the house in a particular region and it help people to find the correct time to buy a home. In this task on House Price Prediction using machine learning, our task is to use data to create a machine learning model to predict house prices in the given region. We will implement a linear regression algorithm on our dataset. By using real world data entities, we are going to predict the price of the house in that area. For better results we require data pre-processing units to improve the efficiency of the model. for this project we are using supervised learning, which is a part of machine learning. We have to go through different attributes of the dataset

Can Economic Fact-Checking Remedy Incorrect Beliefs About Housing Markets?

Supply skepticism is the widely held belief that building more market-rate housing does not help reduce housing prices. Why is supply skepticism a problem, and what is likely to be publicly engaged scholars’ best approach to correcting it? Been et al. have been the leading scholars bringing attention to the phenomenon, and have compiled new economic evidence that increasingly demonstrates that market-rate housing construction reduces housing prices. Will the skeptics who deny the operation of supply and demand in housing markets be persuaded? This seems unlikely. Engaged policy elites who question the benefit of additional housing supply already have well-formed views that are unlikely to be converted by recent, incremental discoveries about housing supply’s price effects. At first glance, the lay public also appears to be supply-skeptical, but their beliefs are only weakly held and unstable. Instead of clear beliefs about markets (skeptical or otherwise), people hold “folk-economic” views that personalize market interactions and blame “bad actors” for high housing prices. They support state housing policies that would punish putative bad actors (e.g., developers, landlords, and Wall Street investors) while showing only tepid and unstable support for building additional market-rate housing. The silver lining for pro-supply advocates is that the average voter’s poorly formed views about housing markets might be susceptible to education and persuasion campaigns that can effectively simplify current research findings.

Unveiling housing preferences amidst flood risks: an empirical study in an emerging market

Purpose This study investigates the interplay between flood risk, financial constraints and buyer behavior in the housing market. It aims to discern how flood experience influences housing choices, particularly among financially constrained buyers. Design/methodology/approach Using hedonic pricing models and probit analysis, this research examines the implicit prices of house characteristics and evaluates the determinants of housing choices, considering factors such as flood risk, financial status and past flood experience. Findings The study underscores that lower house prices incentivize buyers to opt for homes in flood-prone areas, thereby increasing their vulnerability to flood risks. Consequently, financial constraints exacerbate this situation, compelling low-income buyers to accept higher flood risks. However, flood experience fosters risk awareness among buyers, encouraging them to steer clear of flood-prone areas, albeit with lesser efficacy among financially constrained individuals. Originality/value This research contributes to the understanding of how flood risk and financial limitations shape housing decisions. It highlights the role of flood experience in influencing buyer behavior and emphasizes the need for targeted policies to mitigate flood-related risks in the housing market, particularly among economically vulnerable populations.

Identification and Causes of Neighborhood Commercial Areas: Focusing on the Development of Daily Life Circles in Urban Built Environments

Urban planning in China is shifting from an administrative unit-based approach to community life circle planning, aiming to align planning units with residents’ actual activity ranges. As the most fundamental life circle, daily life circle (DLC) planning must adopt a bottom-up approach. However, the widely applicable methods for delineating DLCs remain lacking. This study presents a strategy for delineating DLCs centered on neighborhood commercial areas that aggregate essential daily life services. Correspondingly, a method is proposed for identifying neighborhood commercial areas based on residents’ actual usage of facilities. The method was applied in Qinhuai District, Nanjing, where neighborhood commercial areas were identified and the factors influencing their formation and types were quantitatively analyzed. The results indicate the following: (1) the proposed method accurately identifies neighborhood commercial areas that can serve as DLC central areas; (2) commercial diversity, public transportation stops, and parking spots are the three most influential factors in neighborhood commercial area formation, exhibiting non-linear and threshold effects; and (3) the type of neighborhood commercial areas varies by population density, housing prices, and street betweenness, with betweenness being the most significant factor. These findings provide methods and indicators for DLC delineation and planning, contributing to the realization of the DLC construction concept.

Data‐sparse price indexes by spatio‐temporal regularization and PCA: An application to the Australian housing market

AbstractWe present two novel approaches to overcome the limitations of data‐sparse local house price indexes and combine them into a single model pipeline that is simple, computationally efficient, and interpretable. The first contribution is a new spatio‐temporal regularization of least squares dummy variable models, such as the repeat sales regression used here. This regularization encodes prior knowledge of the proximity of houses in space and their sales in time. It handles missing values in a natural way. The second is nonlocal regularization using truncated principal component analysis (PCA) applied to the resulting national collection of local price indexes. The PCA loadings show that there are important underlying socioeconomic factors that can be leveraged in the construction of Australian market indexes. This PCA reveals important socioeconomic factors, showing that many local markets can be described by a few broad aspects of the national market, consisting of a general trend that contrasts regions influenced by the mining industry with Sydney and Melbourne, and another trend that highlights lifestyle.

An Empirical Study on the Determinants of Housing Prices in Beijing and Model Optimization

This paper aims to investigate the potential factors influencing housing prices in Beijing and optimize the prediction model. Using the Kaggle dataset "Housing Price in Beijing", multiple factors affecting housing prices were examined, including square footage, bedroom count, bathroom count, follower count, the presence of an elevator, and subway proximity. The study initially established a multiple linear regression model (MLR) and then optimized the model through variable selection and hypothesis testing. The final model indicates that square footage, bathroom count, follower count, elevator presence, and subway accessibility significantly impact housing prices. Additionally, subway proximity positively correlates with housing prices, while increased square footage is negatively associated with price, possibly due to lower unit costs for larger properties and the higher market demand for smaller homes. By validating the model's performance using a test dataset, the final model demonstrates effective predictive capability and offers insights for future model improvements.

Are institutional investors the culprit of rising global house prices?

AbstractTaking advantage of the standard regulation of real estate investment trusts (REITs) around the world, we study the implications for housing markets of the entry of institutional investors in 57 cities in 15 countries for the period 2001–2022. We employ an IV approach based on the exogenous demand for REITs by pension funds triggered by changes to the retired population. We show that residential equity REIT capital flows push up multifamily house prices, and are associated with declining rents, potentially affecting households' homeownership versus renting decisions. Estimating a CS‐ECM model, we find that REITs exert long‐run effects on housing markets.

Impact of Natural Disasters on House Prices: A Global Analysis and Policy Implications

This study addresses the limited research on how natural disasters affect house prices by analyzing data from 117 countries between 2000 and 2018, using a panel regression approach to assess these impacts. The results reveal that natural disasters tend to decrease house prices, a finding that holds strong across multiple robustness checks. Specifically, geological disasters are found to have the most significant negative impact on property values. The study also distinguishes between the effects of small and large disasters, with both showing a clear downward effect on house prices. These insights carry important implications for policymakers and investors, as lower house prices in disaster-prone areas may indicate reduced consumption and investment due to the wealth effect, potentially leading to wider economic downturns. To support these regions, economic policymakers might consider measures such as low-tax policies or quantitative easing. The findings also highlight the urgent need for governments to address climate change impacts on the housing market by adopting sustainable technologies and renewable energy sources. Keywords Natural Disasters, House Pricing, Climate Change, Statistical model

Demographic changes, technology growth, and retirement policy reform: Implications for U.S. housing dynamics

As the population aging process continues, concerns about how this situation impacts the housing market and social security rise. To address this question, this paper presents a developed and calibrated general equilibrium life-cycle model incorporating two production sectors to analyze the impacts of demographic structure changes and retirement policy reforms on housing price fluctuations and household choices. The model calibrated to the U.S. macroeconomic data between 1968 and 2018 suggests that in an economy with unlimited land supply, the housing supply curve exhibits perfect elasticity, rendering demographic changes insignificant in housing price fluctuations, while technological advancements lead to decreased prices. A 1% growth in productivity in both sectors results in a 2.6% decrease in house prices. Furthermore, a 1% decrease in population and a 5-year early retirement led to significant reductions of 25% and 30% in individual social security payments and 9% and 18% in interest rates, respectively. This suggests that during a recession caused by demographic structural changes, households become more conservative and prioritize precautionary saving strategies, increasing savings and investing more in housing assets. Consistent with empirical findings, during an economic boom, a decline in the capital-output ratio and the real housing price suggests a decrease in savings and housing asset investment. The rise in consumption drives the capital demand of the non-housing sector to increase, stimulating business expansion and labor inflow.

The Spillover Effects of Public Attention and Real Estate Market: A Case Study from Shanghai, China

The real estate industry is closely linked to the national economy, and fluctuations in house prices have long been a major focus of public attention. This study explores the relationship between public attention and the real estate market, using public attention as a focal point. It systematically explains how public attention affects investment decisions in real estate. Taking Shanghai as a case, this study empirically examines the dynamic time-varying relationship between public attention and house prices, sales, investment and floor space completed by building a Time-Varying Parameter Vector Autoregression (TVP-VAR) model with data from 2011 to 2024.The research shows that the spillover index of house prices on public attention is positive and house prices acting as the sender of the shock, with rising house prices boost public attention. Conversely, the spillover index of public attention on house prices is negative, with public attention acting as the receiver of the shock. Based on the empirical results, it is recommended to incorporate public attention into the real estate market monitoring system, observe the real estate industry using multiple indicators, and enhance the regulation of online media to ensure policy transparency.

An assessment of how street view imagery and remote-sensing data of green and blue spaces can explain variations in housing prices: a case study in Suzhou, China

ABSTRACT Urban blue and green spaces have significant influences on housing prices. In this study, we employed three data sources of blue and green space data sources (street view images, remote-sensing indicators, and land use data) and compared the variations of the three data sources of data in housing prices in Suzhou, China. The Spearman correlation showed street view data and remote sensing data reflect different perspectives of blue and green spaces. In terms of green spaces, street view greenery demonstrated the highest explanatory power for housing prices. This finding suggests that residents favour street greenery than Normalized Difference Vegetation Index (NDVI) and green space land use data. Regarding blue spaces, Normalized Difference Water Index (NDWI) exhibited the highest explanatory power for housing prices. This can be attributed to the fact that NDWI effectively captures water bodies around residential areas. The findings of this study have significant implications for urban planning and real estate development.

Examining the Impact of the Built Environment on Multidimensional Urban Vitality: Using Milk Tea Shops and Coffee Shops as New Indicators of Urban Vitality

Urban vitality is a critical driver of sustainable urban development, significantly contributing to the enhancement of human well-being. A thorough and multidimensional comprehension of urban vitality is essential for shaping future urban planning and policy-making. This study, focused on Chengdu, proposes a framework for assessing various dimensions of UV through the distribution of milk tea and coffee shops. Using random forest and multi-scale geographically weighted regression models, this study investigates the factors influencing urban vitality from both mathematical thresholds and spatial heterogeneity, and develops spatial maps of future vitality to inform targeted urban strategies. The results show that (1) the milk tea index is effective in capturing population vitality, while the coffee index is more closely associated with economic vitality and urban renewal; (2) office buildings (13.46%) and commercial complexes (13.70%) have the most significant impact on both economic and population vitality, while the importance of transportation factors has notably decreased; (3) the influence of these factors demonstrates spatial heterogeneity and nonlinear relationships, with subway station density of 0.5–0.8 stations per kilometer being optimal for stimulating both types of vitality. The minimum threshold for economic vitality in a given unit is a housing price exceeding 6000 RMB/m2; (4) the future vitality map suggests that urban planners should pay greater attention to non-central districts with high development potential. Moreover, spontaneous social interactions and consumer behaviors stimulated by various shops are critical components of urban vitality. In designing the physical environment and urban spatial forms, special attention should be given to enhancing the attractiveness of physical spaces and their capacity to accommodate social interaction.

Research on the Influencing Factors of House Prices based on Multiple Linear Regression Model

Abstract. House prices have risen rapidly, and finding the factors affecting house prices is significant for regulating house prices. Existing literature discusses the factors affecting house prices, but does not analyze the key factors affecting house prices and get a good fit with a multiple linear regression model. Therefore, this paper is of practical significance for the selection and fitting of key variables that explain the rise in house prices. In Spearman's correlation analysis, strong correlation and covariance diagnostics were found for gross product. After linear fitting, it is found that gross product, commercial real estate sales area will have an impact on house prices. The results of this paper on the impact of GDP and commercial real estate sales area on housing prices are conducive to the relevant agencies to take measures to adjust housing prices. At the same time, for people who have the need to buy a house, understanding the main influencing factors is conducive to consumers' initial judgment on the trend of house prices, and then make a decision.

The effects of macroprudential policies on house prices: Evidence from non-ordinary house purchasing policy using China real transaction data

This paper contributes to the international policy debate on the effect of macroprudential policy on housing prices. Our identification relies on the fact that ordinary housing and non-ordinary housing in Beijing face different loan-to-value (LTV) restrictions and transaction tax rates. This paper uses regression discontinuity (RD) design to test whether the differentiated regulation of ordinary housing and non-ordinary housing caused a discontinuous change in housing prices with house-level transaction data. We find that housing prices have a significant discontinuous decline at the cutoff point caused by macroprudential policy, which we take 140 square meters since it is the identification standard of non-ordinary housing. Further, we carry out RD-DID analysis to investigate the influence of loan-to-value (LTV) restrictions on housing prices. We find that around 2% decline in house prices is caused by credit policy.

Inter-regional rail travel and housing markets connectedness between London and other regions

Taking London as a location with which to measure the ripple effect in the UK housing market, this study aims to explain and verify the high degree of correlation between inter-regional transportation and the regional correlation of the housing market. Based on the literature on the relationship between short-term mobility and long-term migration, this paper illustrates that the extent to which people use trains for travel across regions will be related to the ripple effect in the regional housing markets. Frequent railway transport behavior, whether for commuting or traveling, might increase people's desire to relocate between regions, and thus leading to information transmission effects across regional housing prices and transaction volume. First, we estimate a dynamic indicator for the ripple effect. Then, the empirical tests use panel data, including the ripple indicator and passenger number data across time (1996–2022) and regions (nine regions). It is found that if London house prices drive other regional house prices to rise, inter-regional transportation demand will increase, and in turn, the increase of house prices in other housing markets will again drive up London house prices. The number of passengers will affect the information transmitted by the housing market transaction volume in other regions to the London housing market. This implies that higher inter-regional transport needs may lead to migration between London and other property markets, causing their transaction volumes to change in the same direction. The results of this paper verify that travel behavior between regions is a crucial factor in the leading/lagging behavior of regional housing market performance, implying a relationship between short-term travel and long-term migration. The results also indicate that incorporating variables of housing market correlations may help in the prediction of passenger numbers or transportation demand.

Factors influencing housing prices: A comparative study using multiple linear regression and random forest

Abstract. The method is to construct a multiple linear regression model to examine the variables that significantly affect US home prices. The dataset has 13 variables and 545 observations, and it was obtained via Kaggle. This paper also considers comparing the multiple linear regression models and random forest model for predicting house prices and thus concludes which model can most accurately predict house prices. The result indicates that the factors including area, quantity of bedrooms and bathrooms, the existence of a basement, and state of the furnishings, etc. significantly impact housing prices. By comparing Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE), the conclusion suggests that the multiple linear regression model marginally surpasses the random forest regarding the accuracy of the predictions, despite the fact that both models function similarly. This research offers practical applications for enhancing real estate market strategies and provides valuable insights into the factors influencing housing prices.

Evaluating objective and perceived ecosystem service in urban context: An indirect method based on housing market

Ecosystem service (ES) evaluation is usually based on the stocks of natural resources and their functions. However, the value of ES in the urban area depends on human activities more than the existence of natural resources. This research implements an indirect market method by integrating hedonic housing price model to assess ES in urban context from both objective (remote sensing) and subjective perspectives (street view image). Machine learning tools are employed to investigate the impacts of objective and perceived ES on housing prices based on a case study in Wuxi, China. The analytical results suggest that the contribution of ES to house prices in Wuxi ranges from 0% to 10.85%. Further investigation found that visible trees are the most important ES factor of housing price, more important than the coverage of green space. We also find that the quality of blue-green spaces might modify the value of ES, while the poor landscape design and water pollution in the central urban area made the values of ES low in the housing market. This study proves that the indirect method based on the housing market is helpful in valuing ES in the urban context. The high importance of perceived blue-green spaces in ES encourages more efforts on landscape design rather than only increasing coverage.