Determinants Of House Prices Research Articles

Examining the determinants of housing prices and the influence of the spatial–temporal interaction effect: The case of China during 2003–2016

The slowdown of the Chinese economy has been accompanied by a recent rapid rise in housing prices, which has put severe pressure on China's high-quality development. Therefore, understanding the impact of the spatial–temporal interaction effect on housing prices and their potential determinants is critical for formulating housing policies and achieving sustainable urbanization. This study empirically analyzed both of these based on four aspects—the financial market, housing market, housing supply, and housing demand—using 2006–2013 data of 285 prefecture-level (and above) Chinese cities and spatial econometric models. The results indicated that the housing prices of Chinese cities were heavily affected by the interaction effect of space and time, both at the national and regional levels; however, the influence of this interaction effect exhibited a significant spatial differentiation, and only consistently drove up housing prices in Eastern and Western China. Additionally, the regional results based on administrative and economic development levels revealed that wage and medical service levels in first- and second-tier cities had negatively affected the competitiveness and efficiency of the Chinese economy during the investigation period. These findings suggest the need for land supply systems based on the increasing population to prevent housing prices from rising too quickly as well as policies that consider regional variations, accompanied by corresponding supporting measures.

Homeownership Rate and Other House Price Determinants Impacts on New House Prices in Colombia

Homeownership remains a preferred form of tenancy in different parts of the world. The attractions of security, stability, investment potential and a sense of pride outweigh the fear of price instability. For this reason, the Colombian government has encouraged in recent years, various demand policies that have sought to promote the increase in the number of homeowners. However, these ideas could have a severe impact on prices in the real estate market. Therefore, this study seeks to examine the effect of homeownership rate on new house prices in an emerging country with low real estate ownership, credit restrictions and average per capita income. The study uses panel data model to examine the influence of housing tenancy and other variables on the variation of housing prices in Colombia. Data were obtained from various sources including the Central Bank of Colombia, Financial Superintendence of Colombia, and National Administrative Department of Statistics of Colombia. The results show that homeownership rates have a positive effect on the price of new homes, which supports the hypothesis of the research. The population growth of the cities is the factor that is most relevant when explaining the price variations.

Urban Vibrancy: An Emerging Factor that Spatially Influences the Real Estate Market

Urban vibrancy is defined and measured differently in the literature. Originally, it was described as the number of people in and around streets or neighborhoods. Now, it is commonly associated with activity intensity, the diversity of land-use configurations, and the accessibility of a place. The aim of this paper is to study urban vibrancy, its relationship with neighborhood services, and the real estate market. Firstly, it is used a set of neighborhood service variables, and a Principal Component Analysis is performed in order to create a Neighborhood Services Index (NeSI) that is able to identify the most and least vibrant urban areas of a city. Secondly, the influence of urban vibrancy on the listing prices of existing housing is analyzed by performing spatial analyses. To achieve this, the presence of spatial autocorrelation is investigated and spatial clusters are identified. Therefore, spatial autoregressive models are applied to manage spatial effects and to identify the variables that significantly influence the process of housing price determination. The results confirm that housing prices are spatially autocorrelated and highlight that housing prices and NeSI are statistically associated with each other. The identification of the urban areas characterized by different levels of vibrancy and housing prices can effectively support the revision of the urban development plan and its regulatory act, as well as strategic urban policies and actions. Such data analyses support a deep knowledge of the current status quo, which is necessary to drive important changes to develop more efficient, sustainable, and competitive cities.

International Real Estate Review

In many developing countries, house price index construction is sparse, leaving decisions which hinge on housing performance data with little corroboratory evidence. Thus, the purpose of this research is to ascertain the micro-level determinants of house prices in Ghana. Using a qualitative approach, data are collected through semi-structured interviews with twenty expert property practitioners including valuers, academics, property developers, mortgage providers and housing agents. This research uncovers interesting findings including the relevance of unexpired lease terms, and the impacts of market dynamics such as the physical heterogeneity of properties and hearsay. The study also reveals that an index needs to be created and managed through a collaborative effort between the government and industry to ensure wide acceptability. This study lends guidance to housing policy decisions at the local and national levels, and provides a much-needed source of data for further academic inquiry into the housing dynamics in Ghana.

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

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

지역별 주택가격의 격차에 관한 연구 : 회귀분석과 인공신경망 활용

The purpose of this study is to identify the determinants of housing prices in the region based on demand and supply variables and location considering housing preference. In order to analyze the determinants of the housing price among the city areas in the nation, the housing price model was established by using the location conditions considering the supply and housing preference, and the importance was analyzed by regression analysis and artificial neural network. Transportation, job, income, school districts, and educational level, which determine the price of the local housing market, show a strong influence. The model considering the location conditions gives clearer explanation and is more significant than the traditional demand supply model.. Capital was the most important factor in analyzing the importance of artificial neural networks, followed by income, education level, and job. The order of the overall importance is as follows: transportation, job, income, and neighborhood. In the accuracy analysis, artificial neural network analysis to which location variables are added was superior to individual regression analysis model.

Administrative hierarchy, housing market inequality, and multilevel determinants: a cross-level analysis of housing prices in China

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.

Housing attributes and relative house prices in Ghana

PurposeThe study of house prices has become more relevant in recent times after the global financial crisis. Using a housing data set from three regions of Ghana (collated from real estate agents), the purpose of this paper is to estimate the relative importance of housing attributes to house prices.Design/methodology/approachThe hedonic regression analysis conducted indicates that location is the most powerful determinant of house prices. Other relevant factors are the number of bedrooms, the number of floors, the total floor area, land size, age of the house and luxury finishing.FindingsThe implications of these results are many. Policy wise, the study provides an evidence-based empirical study that supports the need for better urban planning to improve communities, which in turn is associated with house price appreciations. Homeowners, investors and creditors, particularly mortgage lenders could be the immediate beneficiaries. Drawing on this, improved urban planning could mitigate strategic defaults that results from house prices falling below mortgage loan balances. This is important for financial market stability.Originality/valueThe paper provides a comprehensive and unique understanding of the hedonic determinants of house prices in Ghana. Future studies could examine the effect of location upon mortgage lending in Ghana.

Housing prices in peninsular Malaysia: supported by income, foreign inflow or speculation?

PurposeThis paper is motivated by a concern about the ability of the average Malaysian income to catch up with the rapidly increasing house prices in Peninsular Malaysia. Financial innovation in financial system now regards houses as a financial asset and speculation vehicle. Therefore, a house purchase is made to acquire not merely a necessity but also a financial asset which can generate future returns. Given the problems in the housing market, this paper aims to examine the determinants of house prices in Malaysia, including those such as income, population, foreign inflow and speculation.Design/methodology/approachThis study adopts panel data analyses, namely, the fixed effect model (FEM) and the pooled mean group (PMG), and uses data at state level in quarterly frequency, spanning from 2005Q1 to 2013Q4.FindingsBased on the results of FEM, these determinants influence house prices significantly. Moreover, the PMG results suggest that there is convergence in the model, which are indicated by the significant and negative sign of the error correction term. In conclusion, the rapidly increasing house price is not caused by speculation activities in the housing market. More precisely, Malaysian income is capable of catching up with the increasing house prices.Practical implicationsAs income remains to be one of the major drivers in influencing Malaysian house price, Malaysian Government shall continue the policies of supply low cost houses to the low-income groups and My First Home Scheme (SRP) by offering less stringent rules in applying house loan for the first-time house buyers.Originality/valueThis study used the actual data of foreign housing purchase obtained from Malaysia Valuation and Property Services Department to represent foreign inflow; therefore, the results will reflect the impact of foreign inflow in a better manner.

Determinants of new housing prices in Medellín: A structural model.

Determinants of new housing prices in Medellín: A structural model.

Modelling housing prices and market fundamentals: evidence from the Sydney housing market

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.

Determinants of housing prices in Baku: empirical analyses

PurposeThis paper aims to reveal major factors affecting housing prices (flats and houses) in Baku, the capital of Azerbaijan Republic.Design/methodology/approachBased on cross-sectional data set of 497 flats and 443 houses, polynomial regression models are estimated for flats and houses separately. Regression models are estimated by using ordinary least squares.FindingsLocation, largeness, repair level and existence of bill of sale are major price determinants for flats. For houses, number of rooms also matters. Findings reveals that houses are land intensive (more floors, less land area) toward city center, and vice versa. Price difference due to existence of bill of sale diminishes significantly toward the surrounding areas.Research limitations/implicationsThe data set represents view of sellers and does not take into consideration price bargaining in time of sale; probability of information asymmetries exists which not could accounted for, and urgency of sale is not considered.Practical implicationsEstimation results can be used for housing valuation by real estate market participants and investors.Social implicationsResearch findings reveal importance of bill of sale as a major price determinant and expected to attract policymakers’ attention to solve such a big social problem. Additionally, models can be based for price estimations in Baku housing market.Originality/valueThe study contributes to the literature by empirically analyzing housing market in Baku, Azerbaijan. Research produces new practically valuable findings.

주택가격 결정에 미치는 주요 요인에 관한 연구

현실적으로 우리 사회에서 주택 가격의 안정화는 매우 중요하다. 특히 정부에서의 주택가격 안정화 정책은 매우 중요하다. 무엇보다도 주택가격의 안정화를 위해서는 다음과 같은 사안들이 중요하다.BR 첫째, 그 동안 우리나라 주택정책의 성격은 규제의 강화와 완화의 반복 정책이었다고 할 수 있다. 즉 우리나라의 주택정책은 크게 주택가격의 안정을 위한 투기억제와 경기부양책으로 일관하였다. 그럼에도 불구하고 수도권 지역과 지방의 주택가격의 양극화와 주택시장의 침체 현상과 다주택자의 양산으로 인한 무주택자의 갈등 등 부작용이 발생하였다. 그러므로 정부에서는 정책의 일관성으로 주택시장에 적합환 규제의 적절성이 유지되어야 한다. 즉 수도권과 지역간의 격차해소, 다주택자를 위한 규제와 무주택자를 위한 공급정책 등이 선행되어야 할 것이다.BR 둘째, 이미 우리나라는 주택보급률이 100%를 넘은 시점에서 실제 자가보유율은 그 절반정도 밖에 이르지 못하고 있는 실정이다. 그 이유는 다주택자의 양산과 저출산 고령화 사회 현상, 빈부의 격차 심화 등으로 인한 수급불균형으로 인한 자가주택 부족이 발생하였다. 그러므로 향후 주택의 유효수요를 충족하기 위해서는 적절한 주택의 공급이 유지되어야 할 것이다. 무엇보다도 과잉공급된 부분을 규제하는 방안이 고려된다. 주택수요 억제를 위해서 다주택자에 대한 규제강화, 과잉공급된 수도권 규제강화, 투기 규제강화, 수요충족을 위한 재건축ㆍ재개발 억제, 수요를 위한 주택재고, 주택비축 등이 고려된다.BR 셋째, 주택의 공급도 수요에 따라 적정하게 공급되어야 한다. 공급문제는 주로 정밀한 주택현황 분석이나 장기적인 주택의 수요 측정이 안되거나 공급 규모의 추정이 부실한 경우와 과잉공급으로 인한 미분양사태가 발생하므로, 특히 과잉공급된 수도권의 공급억제와 다주택자에 대한 사전방지와 자가 점유율을 위한 청년층과 서민을 위한 주택 수급이 선행되어야 할 것이다. 그러기 위해서는 저출산 고령화사회에 대한 대책, 생산인구의 증가대책, 경제활성화, 빈부의 격차 해소 등이 선행되어야 할 것이다.BR 넷째, 주택의 필요 계층에서 주택을 구입함(구매 및 임대)에 있어서 적절한 대출은 소비자에게 상당히 유리하다. 문제는 전세대출을 활용해 ‘갭 투자’를 하거나 ‘주식에 투자’하는 등 악용하는 사례가 발생하여 결국 가게 부채가 증가하게 되고 결국에 가서는 정부의 부담이 되는 사례가 발생하게 된다. 또 저금리하에서는 임대인(집주인)들이 월세를 선호하게 되어 임차인들에게 부담이 되기도 하며 금리인상에 있어서도 이자 부담이 크게된다. 그러므로 가계대출을 더욱 규제(LTV와 DTI 강화)하는 방안이 검토되어야 하고 총 부채원리금상환비율(DSR)도 규제하여야 하며1) 그리고 금리인상에 따른 고정금리 전환과 그에 따른 프라이싱기법이 고려된다.BR 이상과 같이 주택가격의 안정화를 위한 정부의 주택정책에서 발생하는 문제점에 대한 개선방안의 구체적인 실현을 위하여는 정부의 주택정책의 안정화 내지 일관성, 주택시장 원리에 입각한 수요와 공급의 적정성 유지, 금리인하와 인상을 위한 규제책 등을 제언하였다. 다만 이의 구체적인 실현을 위해서는 무엇보다도 정확한 데이터를 통한 주택계획의 절차화와 제도화, 그리고 시장친화적인 주택의 수요와 공급에 있어서 범위와 한계 설정, 아울러 금리변동에 대한 통제장치 설정 등으로 주택가격의 안정화는 물론 국민의 주거안정에도 이바지해야 할 것이다.

House prices and immovable property tax: Evidence from OECD countries

AbstractThis paper studies the impact of changes in immovable property tax revenues on the growth rate of house prices by analysing a panel of 34 OECD countries over the period 1970–2014. Starting from the annual series of immovable property tax revenues, we isolate years of significant shifts in the property tax regime and study their impact on house prices. We find a strong negative relationship between increases in immovable property tax revenues and house prices. This relationship is robust to the inclusion of cyclical determinants of house prices, country and year fixed effects, and country‐specific linear trends. We also propose an instrumental variable strategy based on countries’ legal origins that confirms a statistically significant negative impact of such taxes in the short run.

Continuously Updated Indirect Inference in Heteroskedastic Spatial Models

Spatial units typically vary over many of their characteristics, introducing potential unobserved heterogeneity which invalidates commonly used homoskedasticity conditions. In the presence of unobserved heteroskedasticity, standard methods based on the (quasi-)likelihood function generally produce inconsistent estimates of both the spatial parameter and the coefficients of the exogenous regressors. A robust generalized method of moments estimator as well as a modified likelihood method have been proposed in the literature to address this issue. The present paper constructs an alternative indirect inference approach which relies on a simple ordinary least squares procedure as its starting point. Heteroskedasticity is accommodated by utilizing a new version of continuous updating that is applied within the indirect inference procedure to take account of the parametrization of the variance-covariance matrix of the disturbances. Finite sample performance of the new estimator is assessed in a Monte Carlo study and found to offer advantages over existing methods. The approach is implemented in an empirical application to house price data in the Boston area, where it is found that spatial effects in house price determination are much more significant under robustification to heterogeneity in the equation errors.

Accuracy and Determinants of Self-Assessed Euro Area House Prices

Using microdata from the second wave of the Household Finance and Consumption Survey, we investigate the accuracy of property values estimated by homeowners - so called “self-assessed” house prices - and explore the drivers of possible deviations of these prices from official hedonic house price indices. We find evidence that euro area homeowners overestimate the value of their properties by around 9%. Across the largest euro area countries, the overestimation lies in a range between 3.2% in Germany and 22% in Italy. Household characteristics, including the level of indebtedness, appear to explain significant discrepancies between hedonic and self-assessed house price indices, while the limited available data related to property characteristics are generally not affecting this gap. For the euro area, we find that higher self-assessed house prices are associated with a mild increase in consumption expenditures.

Determinantes del precio de la vivienda nueva en Medellín: un modelo estructural

Esta investigación busca, mediante un modelo estructural, identificar los determinantes del precio de la vivienda nueva no VIS en Medellín, con el fin de explicar la formación de precios desde la organización industrial del sector. Los resultados indican que el empleo, el crecimiento económico y las políticas públicas (subsidios) influyen directamente -la inflación tiene injerencia negativa- sobre las condiciones del mercado de vivienda (CMV); simultáneamente, las CMV afectan la oferta y la demanda, fuerzas que, a su vez, inciden en la formación de los precios de mercado. Además, identifica la incidencia de las políticas públicas en el aumento de los precios.

Highly Disaggregated Land Unavailability

We use new large-scale data techniques and comprehensive high resolution satellite imagery for the contiguous United States to construct novel datasets that capture the geographic determinants of house prices and housing supply: The percentage of undevelopable land – Land Unavailability – and its complement, buildable land. Our Land Unavailability measure expands on the popular proxy from Saiz (2010) by: (1) using higher resolution satellite imagery from the USGS; (2) more accurate geographic boundaries; and (3) multiple levels of disaggregation. Using highly disaggregated data we show that Land Unavailability is uncorrelated with housing demand proxies, validating Land Unavailability as an instrument for house prices; that the geographic components of Land Unavailability, especially in combination with modern machine learning techniques, provide substantial incremental predictive power for house prices; and previous studies that employed limited land unavailability datasets underestimated the impact of house prices on unemployment during the Great Recession by 30%. With our buildable land dataset we then test the supply side speculation theory that aims to explain the previously puzzling large house price growth in traditionally elastic housing markets. In line with theory, results document that housing markets with intermediate amounts of buildable land, those that are elastic in the short run but plausibly inelastic in the long run, experienced abnormally large house price growth during the 2000s.

Price Determinants and GIS Analysis of the Housing Market in Vietnam: The Cases of Ho Chi Minh City and Hanoi

Since the introduction of the Doi Moi economic reform in 1986, the real estate market in Vietnam has witnessed a sharp increase in foreign investment inflows and a remarkable growth in the housing market, particularly for high-rise apartments in large cities. This study investigates the determinants of apartment prices in Ho Chi Minh City (HCMC) and Hanoi, the two most representative cities in Vietnam. The spatial distribution of apartments and their price determinants were addressed by the spatial analysis of Geographic Information System (GIS) and the hedonic model. The price determinants of both cities were closely associated with downtown-related factors; moreover, the externalities of urbanization affected each city. While HCMC was more related to the locational attributes of urban amenities and community density as well as programs because of unmanaged urbanization, Hanoi was more related to housing attributes, since the majority of apartment projects were developed under urban infrastructure development supported by the central government. Apartment cluster maps of each city clearly show the contrast of housing distribution. Our findings clarify the impact of government policies on housing price determinants and can be a reference for private- and public-sector stakeholders seeking to undertake economically and socially sustainable housing development projects in Vietnam.

Spatial Effects of Public Service Facilities Accessibility on Housing Prices: A Case Study of Xi’an, China

The premium effect of public service facilities on the housing market is a critical determinant of housing prices, leading to the competition of different social groups in the housing market and fueling spatial inequality. Taking Xi’an, China as a case in point, this study uses Geographic Information System (GIS) to describe the spatial distribution pattern of housing prices and urban public service facilities. Using the mixed geographically weighted regression model (MGWR) and the geographical detector model (GD), this study reveals the spatial effects of these facilities’ accessibility on housing prices. The results show that commercial and leisure facilities are spatially stationary, whereas a non-stationary effect is observed among those providing educational, medical, cultural, sport, and financial services. From the urban spatial resource allocation perspective, facilities meeting people’s basic needs, such as medical care and education, constitute the basic elements of housing price differentiation. When any two of these interact, a bivariate-enhanced effect emerges. The decisive interactive elements of housing price differentiation involve the facilities meeting people’s higher-level needs, such as leisure, culture, sports, and finance. When these interactive elements interact with other facilities, a non-linear enhancement effect is induced. This research is of practical value for improving people’s living quality, optimizing the spatial distribution of public service facilities, and eliminating urban spatial inequality.