Nominal loss aversion and equity constraints in house price determination: Empirical evidence in the absence of down-payment constraints

PurposeHousing prices in Sydney have increased rapidly in the past three decades. This leads to a debate of whether Sydney housing prices have departed from macroeconomic fundamentals. However, little research has been devoted to this area. Therefore, this study aims to fill this gap by examining the long-run association between housing prices and market fundamentals. Further, it also examines the long-run determinants of housing prices in Greater Sydney.Design/methodology/approachThe analysis of this study involves two stages. The first stage is to estimate the presence of long-run relationship between housing prices and market fundamentals with the Johansen and Juselius Cointegration test. Thereafter, the determinants of housing prices in Greater Sydney is assessed by using a vector error correction model.FindingsThe empirical results show that Sydney housing prices are cointegrated with market fundamentals in the long run. In addition, there is evidence to suggest that market fundamentals such as gross disposable income, housing supply, unemployment rate and gross domestic product are the key long-run determinants of Sydney housing prices, reflecting that Sydney housing prices, in general, can be explained by market fundamentals in the long run.Research limitations/implicationsThe findings enable more informed and practical policy and investment decision-making regarding the relation between housing prices and market fundamentals.Originality/valueThis paper is the first study to offer empirical evidence of the degree to which the behaviour of housing prices can be explained by market fundamentals, from a capital city instead of at a national level, using a relatively disaggregated dataset of housing price series for Greater Sydney.

This paper uses almost 200 years of historical data on house prices and its determinants from Amsterdam, the Netherlands. We find that before 1900 population growth, construction costs and new housing supply are the most important determinants of house price dynamics. After 1900 income starts to play a role and, with the development of the mortgage market, interest rates as well. Directly after World War II population and new housing supply are the key determinants of house prices, which likely reflects the baby boom generation and post-war reconstruction plans. Our results imply that the determinants of house prices are not fixed and reflect the economic state of affairs in each different era.

Determinants of housing prices: evidence from Ontario cities, 2001-2011

How risky are investments in residential real estate? To answer this question, information is needed about the behavior of house prices. The hedonic methodology has become a standard approach for modelling the prices of heterogeneous assets. Although intuitively appealing, it is often criticized that this approach has no sound theoretical background. We have developed a model that partly circumvents this criticism. Based on an approximation for the present value, our model delivers a state space form for the determination of house prices. Thus, we can incorporate in an economically meaningful way other economic variables like the inflation rate, mortgage rates and returns of other assets. Under some restrictive conditions, our model reduces to the standard hedonic approach. We use the EM algorithm with a final scoring step to estimate our model with monthly data of single-family house sales from the four South-West districts of Berlin for the years 1982:7 to 1999:12.

Based on 5410 sold residential projects, this paper used Kriging and the Geographically Weighted Regression (GWR) model to reveal the characteristics and determinants of housing prices in Chongqing. The results show that Chongqing’s housing prices formed a clear polycentric pattern: peaking at the major Central Business District (CBD) of Jiefangbei and sub-peaking at several Secondary Business Districts (SBDs). The pattern of housing prices was found to be influenced by accessibility of urban centers, urban facilities (such as schools, hospitals, metroline), Yangtze and Jialing rivers. Considering spatial non-stationary of housing prices, GWR model was further employed to reduce spatial dependency of data. The results of GWR model show that the more accessible to the nearest urban center is, the higher housing prices are; the nearer to rivers is, the higher housing prices are; the more proximity to metrolines, the higher housing prices are. To sum up, polycentric urban development is regarded as the underlying factors shaping housing prices in mountain Chongqing.

Determinants of housing prices are particularly significant for monitoring and understanding housing prices. Traditional variables are measured through official statistics or questionnaire surveys, which are labour intensive and time-consuming. New forms of data, such as point of interest or street view imagery, have been used to extract housing location and neighbourhood features, but they cannot capture how different individuals recognised and evaluated the properties nearby, which may also be relevant in the house price estimation. Therefore, this study investigates whether user-generated images may be used to monitor and understand housing prices and how they influence real estate values. Within this context, perceived scenes features are extracted and quantified to blend with commonly used determinants of housing prices. Two machine learning algorithms, random forest and gradient boosting machines, are utilised and deployed for integration with a typical housing price modelling-hedonic price model. By comparing the performance and interpretability of different models, the relative importance of features and how they influence the estimation power of the models is visualised and analysed. The findings suggest that random forest predictions perform the best and are interpretable, with geotagged Flickr images adding 4.6% to the model’s accuracy (R2) from 61.9% to 66.5%. Although user-generated images increase minor value in house price estimation, they may be used as a supplementary data source to capture perception features for house price estimation. This could help the restructuring and optimisation of residential areas in future regional construction, planning and development.

Numerous studies have examined the determinants of housing prices in Chinese cities. However, most of them ignored the hierarchical structural characteristics of housing prices because of the use of ordinary least squares and hedonic pricing to model the housing prices for a single city. Therefore, this study explores the multi-level determinants of housing prices and their interactions at different levels. To this end, it proposes a three-level hierarchical linear model (HLM) using observations from 146,099 communities nested in 1120 counties of 31 provinces in China as a case study. The results of the hierarchical linear regression indicate significant variances in average housing prices. This finding suggests HLM to be appropriate when dealing with housing prices inherently nested at multiple geographic levels. Overall, housing values in China are not only determined by accessibility factors but are also driven by multi-level socioeconomic aspects. Among the selected variables, high-speed railway shows a significant positive effect, while ordinary railway shows a significant negative effect on housing values at the community level. At the county level, rural–urban migration and per capita living space have significant positive impacts on housing value. At the province level, the relationship between rural–urban migration and housing prices depends on economic development and urban employment. Similarly, the average wages of urban employment influence the relationship between per capita living space and housing prices. These results suggest that contextual effects exist between the determinants of housing prices at county and province levels.

This study investigates the spatial and temporal dynamics of housing prices in Serbia, addressing the critical need to understand the drivers of real estate prices and their implications for economic and social welfare. Employing a panel data analysis approach on a unique dataset covering 24 distinct urban areas in Serbia from 2011 to 2021, we examine the relevance of diverse economic, demographic, and infrastructural indicators, providing novel insights within a developing country context. Our findings reveal that the housing market stock-flow model effectively predicts housing price appreciation trends, explaining over 60 percent of variation in property prices. Notably, disparities in labour income, captured by average wages and registered employment rates, emerge as significant determinants of real estate prices, underlining socio-economic disparities within Serbian cities. Housing prices exhibit a positive response to the population/housing stock ratio, suggesting higher prices in cities experiencing faster population growth relative to housing supply. Intensified construction is associated with elevated housing prices. Additionally, we find positive association between the inflation variable and housing prices, underlining real estate’s potential as an inflation hedge. Public service provision and infrastructural amenities also emerge as contributors to higher housing prices in urban areas, emphasizing the importance of comprehensive urban planning strategies. Our study contributes to the literature by providing specific quantitative evidence, advancing the understanding of urban housing market dynamics in developing countries. By offering nuanced insights into determinants of housing prices, our research informs policymakers and urban planners seeking to foster equitable and sustainable urban development strategies.

This paper is trying to analyze the determinants of housing prices in an oil-based economy, where the price of oil plays a major role in such economies. It is also common to find that government spending represents the most important component of aggregate spending and that governments usually play a central role in the provision of public services to citizens at substantial subsidies, housing is on top of them. The paper is trying to identify the role played by oil price in the housing market in Kuwait. The model is composed of four major determinants of house prices including the price of oil, government expenditures, inflation rate and interest rate. Results confirm the role played by the four factors in determining the price of houses. Variance decomposition indicates that up to 10 quarters, 94.3% of the forecast error variance in housing prices is explained by house price itself, whereas, only 2.3%, 1.6%, 1.5% and 0.8% are explained by Interest rates, inflation rates, government expenditures, and price of oil respectively. The oil price does not seem to play an important impact on price changes in Kuwait. One important recommendation is for the government to relax its monopoly on land and invite the private sector to come up with housing solutions to increase the supply of houses in the private housing market and reduce the upward pressures on house prices.

Fundamental determinants of housing prices which affect housing demand and supply are the most common in developed countries. These are economic and financial determinants as well as demographic indicators. However, housing price analysis in less developed countries submit controversial and not sufficient results about the impact of interest rate, inflation and unemployment. Moreover, it does not investigate the influence of demographic variables and the means of economic policy. In this article the effect of GDP, unemployment, inflation, interest rate, emigration and the means of macroprudential policy on housing prices in Lithuania was evaluated. The results showed that inflation, interest rate and emigration are not causal determinants of housing prices, which mostly depend on GDP, unemployment, the means of macroprudential policy and the average housing prices in the previous period.

Residence is basic need of life. Sale and purchase of house is common practice among the society. To investigate the factors that affects house pricing, researchers condicted this study. This study is based on influencing factors or determinants of housing prices in district Mardan. The study has collected primary data through a questionnaire, using a stratified random sampling technique. The data was collected from 250 house buyers; the buyers were selected on the bases of listing done through property dealers. The respondents were selected on the base of house buyers, who bought a house in the last 5 to 10 years. The study has used a multiple regression model. The study found that house size, number of floors, and electricity connection in a house increase house prices. Bad smell and distance to urban areas negatively affect house prices in district Mardan.

Loss aversion, equity constraints and seller behavior in the real estate market

This paper studies the determinants of house prices in eight transition economies of central and eastern Europe (CEE) and 19 OECD countries. The main question addressed is whether the conventional fundamental determinants of house prices, such as GDP per capita, real interest rates, housing credit and demographic factors, have driven the observed house prices in CEE. We show that house prices in CEE can be explained well by the underlying conventional fundamentals and some transition specific factors, in particular institutional development of housing markets and housing finance and quality effects.

This paper exploits the significant recent variation in U.S. house prices to empirically examine the effect on housing equity constraints and nominal loss aversion on household mobility. The analysis uses unique, detailed data from 1985-1996 on household characteristics, mobility, and wealth from the National Longitudinal Survey of Youth (NLSY79) matched with house price data from 149 metropolitan areas to estimate semi-parametric proportional hazard models of intra- and inter-metropolitan mobility. There are five principal findings. First, household intra-metropolitan own-to-own mobility responds differently to nominal housing losses than to gains. Second, nominal loss aversion is significantly less pronounced in intra-metropolitan own-to-rent and inter-metropolitan mobility, respectively. Third, there is some evidence of binding equity constraints in intra-metropolitan own-to-own mobility. Fourth, there is little evidence that low equity constrains intra-metropolitan own-to-rent and inter-metropolitan mobility, respectively. Fifth, a comparison of the estimated effects indicates that nominal loss aversion has a more dominant effect than equity constraints in restricting household mobility: roughly two-and-a-half to three times the impact of equity constraints.

This paper exploits the significant recent variation in United States house prices to empirically examine the effect on housing equity constraints and nominal loss aversion on household mobility. The analysis uses unique, detailed data from 1985-1996 on household characteristics, mobility, and wealth from the National Longitudinal Survey of Youth (NLSY79) matched with house price data from 149 metropolitan areas to estimate semiparametric proportional hazard models of intra- and inter-metropolitan mobility. There are five principal findings: (1) household intra-metropolitan own-to-own mobility responds differently to nominal housing losses than to gains; (2) nominal loss aversion is significantly less pronounced in intra-metropolitan own-to-rent and inter-metropolitan mobility, respectively; (3) there is some evidence of binding equity constraints in intra-metropolitan own-to-own mobility; (4) there is little evidence that low equity constrains intra-metropolitan own-to-rent and inter-metropolitan mobility, respectively; (5) a comparison of the estimated effects indicates that nominal loss aversion has a more dominant effect than equity constraints in restricting household mobility, roughly two and one-half to three times the impact of equity constraints.