Residential properties, resources of basic education and willingness price of buyers

2014 ◽  
Vol 4 (3) ◽  
pp. 227-242 ◽  
Author(s):  
Hao Zhang ◽  
Zhong-fei Li
Keyword(s):  
Basic Education ◽  
Housing Prices ◽  
Housing Price ◽  
Educational Resources ◽  
Allocation Mechanism ◽  
Content Type ◽  
Education Resources ◽  
Standards Of Living ◽  
Residential Properties ◽  
The Individual

Purpose – China's resource allocation mechanism in education has become an important factor in determining residential access to educational resources. The purpose of this paper is to analyze the impacts made by the individual natures of buyers, the external environment, as well as the characteristics of residential properties on the willingness price of buyers. The study's aim is to lay theoretical foundations for the determination of problems related with the matters under consideration. Design/methodology/approach – Using the panel data of 54 districts and counties in Beijing, Shanghai, Guangzhou and Shenzhen, the study unifies macro factors and micro factors in a model for empirical analysis. Findings – Basic education resources can affect housing prices via the “capitalization of education.” The degree of those educational resources’ influence on willingness price changes according to personal income levels, standards of living, housing price fluctuations, the convenience of the residential area and the degrees of urbanization in a district. The greater the buyer's income and standard of living is, the higher is their willingness price. Buyers in urbanized areas prefer increases in educational resources. Increased educational resources increase the values of residential downtown areas. In developed areas with private educational facilities, the role of educational resources in influencing property prices is relatively small. Originality/value – This paper uses data concerning the consumption and investment of residential properties to build a theoretical model for the willingness price of buyers. It unifies macro factors and micro factors in a single model and presents new results about basic education resources and the willingness price of buyers under different conditions.

scholarly journals Residential property market in Malaysia: an analysis of price drivers and co-movements

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Hassanudin Mohd Thas Thaker ◽  
Mohamed Ariff ◽  
Niviethan Rao Subramaniam
Keyword(s):  
Housing Prices ◽  
Housing Price ◽  
Government Support ◽  
Policy Implications ◽  
Kuala Lumpur ◽  
Content Type ◽  
Time Frequency ◽  
Long Run ◽  
Residential Properties ◽  
Practical Implications

PurposeThe purpose of this paper is to identify the drivers of residential price as well as the degree co-movement of housing among different states in Malaysia.Design/methodology/approachThis study adopted an advanced econometrics technique: the dynamic autoregressive-distributed lag (DARDL) and – the time-frequency domain approach known as the wavelet coherence test. The DARDL model was applied to identify the cointegrating relationships and the CWT was used to analyze the co-movement and lead–lag relationships among four states’ regional housing prices. The extracted data were mainly on annual basis and comprised macroeconomics and financial factors. Information with regard to residential prices and other variables was extracted from the National Property Information Centre (NAPIC) website, the Central Bank of Malaysia Statistics Report, the Department of Statistics, Malaysia, I-Property.com and the World Bank (WB). The data covered in this study were the pool data from four main states in Malaysia and different categories of residential properties.FindingsThe empirical results indicate that there were long-run cointegration relationships between the housing price and capital gain and loss, rental per square feet, disposable income, inflation, number of marriages, deposit rate, risk premium and loan-to-value (LTV) ratio. While the wavelet analysis shows that (1) in the long run, Kuala Lumpur housing price having strong co-movement with Selangor, Penang and Melaka housing prices except for Johor and (2) the lead–lag relationship also postulates Kuala Lumpur housing price having in-phase category with Selangor, Penang and Melaka housing prices except for Johor.Practical implicationsThis study offers relevant practical implications. First, the study proposes an active collaboration between the private sector and government support which may help to smooth the pricing issue of residential properties. More low-cost residential projects are needed for focus groups including middle- and low-income earners. Furthermore, the results are expected to provide real estate investor in Malaysia, an improved understanding of the regional housing market price dynamics.Originality/valueThe findings of this study were obtained from various reliable sources; therefore, the results reflected the analysis of price drivers and co-movements. Furthermore, findings from this study lend some support to the argument on the rise of residential prices and offer several policy implications from a practical point of view with regard to the residential market.

Dynamic effects and spatial heterogeneity of land supply on housing price: evidence from Nanchang, China

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Zhijiang Wu ◽  
Yongxiang Wang ◽  
Wei Liu
Keyword(s):  
Spatial Heterogeneity ◽  
House Prices ◽  
Housing Prices ◽  
Housing Price ◽  
Dynamic Effects ◽  
Negative Effects ◽  
Land Supply ◽  
Content Type ◽  
Major Factors ◽  
The City

Purpose Economic fundamentals are recognized as determining factors for housing on the city level, but the relationship between housing price and land supply has been disputed. This study aims to examine what kind of impact housing prices have on land supply and whether there is heterogeneity in different regional spaces. Design/methodology/approach This study collects the relevant data of land supply and housing prices in Nanchang from 2010 to 2018, constructs a vector autoregression (VAR) model, including one external factor and four internal factors of land supply to explore the dynamic effects and spatial heterogeneity of land supply on housing prices through regression analysis. Also, the authors use the geographic detector to analyze the spatial heterogeneity of housing prices in Nanchang. Findings This study found that the interaction between land supply and housing price is extremely complex because of the significant differences in the study area; the variables of land supply have both positive and negative effects on housing price, and the actual effect varies with the region; and residential land and GDP are the two major factors leading to the spatial heterogeneity in housing price. Research limitations/implications The dynamic effects of land supply on housing price are mainly reflected in the center and edge of the city, the new development area, and the old town, which is consistent with the spatial pattern of the double core, three circles and five groups in Nanchang. Originality/value This is a novel work to analyze the dynamic effects of land supply on house prices, instead of a single amount of land supply or land prices. Furthermore, the authors also explore the spatial heterogeneity according to the regional characteristics, which is conducive to targeted policymaking.

Property investor decisions using income and rental ratio signals

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Billie Ann Brotman
Keyword(s):  
Real Estate ◽  
Housing Market ◽  
Discount Rate ◽  
Price Index ◽  
Housing Prices ◽  
Housing Price ◽  
Consumer Sentiment ◽  
Content Type ◽  
Income Ratio ◽  
Real Estate Prices

PurposeThis paper, a case study, aims to consider whether the income ratio and rental ratio tracks the formation of residential housing price spikes and their collapse. The ratios are measuring the risk associated with house price stability. They may signal whether a real estate investor should consider purchasing real property, continue holding it or consider selling it. The Federal Reserve Bank of Dallas (Dallas Fed) calculates and publishes income ratios for Organization for Economic Cooperation and Development countries to measure “irrational exuberance,” which is a measure of housing price risk for a given country's housing market. The USA is a member of the organization. The income ratio idea is being repurposed to act as a buy/sell signal for real estate investors.Design/methodology/approachThe income ratio calculated by the Dallas Fed and this case study's ratio were date-stamped and graphed to determine whether the 2006–2008 housing “bubble and burst” could be visually detected. An ordinary least squares regression with the data transformed into logs and a regression with structural data breaks for the years 1990 through 2019 were modeled using the independent variables income ratio, rent ratio and the University of Michigan Consumer Sentiment Index. The descriptive statistics show a gradual increase in the ratios prior to exposure to an unexpected, exogenous financial shock, which took several months to grow and collapse. The regression analysis with breaks indicates that the income ratio can predict changes in housing prices using a lead of 2 months.FindingsThe gradual increases in the ratios with predetermine limits set by the real estate investor may trigger a sell decision when a specified rate is reached for the ratios even when housing prices are still rising. The independent variables were significant, but the rent ratio had the correct sign only with the regression with time breaks model was used. The housing spike using the Dallas Fed's income ratio and this study's income ratio indicated that the housing boom and collapse occurred rapidly. The boom does not appear to be a continuous housing price increase followed by a sudden price drop when ratio analysis is used. The income ratio is significant through time, but the rental ratio and Consumer Sentiment Index are insignificant for multiple-time breaks.Research limitations/implicationsInvestors should consider the relative prices of residential housing in a neighborhood when purchasing a property coupled with income and rental ratio trends that are taking place in the local market. High relative income ratios may signal that when an unexpected adverse event occurs the housing market may enter a state of crisis. The relative housing prices to income ratio indicates there is rising housing price stability risk. Aggregate data for the country are used, whereas real estate prices are also significantly impacted by local conditions.Practical implicationsRatio trends might enable real estate investors and homeowners to determine when to sell real estate investments prior to a price collapse and preserve wealth, which would otherwise result in the loss of equity. Higher exuberance ratios should result in an increase in the discount rate, which results in lower valuations as measured by the formula net operating income dividend by the discount rate. It can also signal when to start reinvesting in real estate, because real estate prices are rising, and the ratios are relative low compared to income.Social implicationsThe graphical descriptive depictions seem to suggest that government intervention into the housing market while a spike is forming may not be possible due to the speed with which a spike forms and collapses. Expected income declines would cause the income ratios to change and signal that housing prices will start declining. Both the income and rental ratios in the US housing market have continued to increase since 2008.Originality/valueA consumer sentiment variable was added to the analysis. Prior researchers have suggested adding a consumer sentiment explanatory variable to the model. The results generated for this variable were counterintuitive. The Federal Housing Finance Agency (FHFA) price index results signaled a change during a different year than when the S&P/Case–Shiller Home Price Index is used. Many prior studies used the FHFA price index. They emphasized regulatory issues associated with changing exuberance ratio levels. This case study applies these ideas to measure relative increases in risk, which should impact the discount rate used to estimate the intrinsic value of a residential property.

Housing price and enterprise financing: does mortgage effect exist?

2019 ◽  
Vol 9 (1) ◽  
pp. 137-152 ◽  
Author(s):  
Shujing Li ◽  
Nan Gao
Keyword(s):  
Financial Development ◽  
Design Methodology ◽  
Empirical Test ◽  
Housing Prices ◽  
Housing Price ◽  
Fixed Effect Model ◽  
Content Type ◽  
Empirical Results ◽  
Effect Model ◽  
High Level

Purpose The purpose of this paper is to explore the influence of the rise in housing prices on enterprise financing and also the sustainability and heterogeneity of this effect. Design/methodology/approach Empirical test, panel data, fixed-effect model, IV and 2SLS were used in this paper. Findings The empirical results indicate that the mortgage effect does exist, and the authors further analyze the heterogeneity of this effect by dividing the sample based on the degree of financial development and property rights; the empirical results reveal that the mortgage effect is significantly higher in places with the high level of financial development. Besides, compared to the SOE enterprise, the mortgage effect has more influence on non-SOE companies. Research limitations/implications The results indicate that the mortgage effect should be considered when regulating housing market, and in order to improve the financing capability of company, its profitability and financial market efficiency should be emphasized. Originality/value This paper not only confirms the existence of the mortgage effect, but also explores its sustainability and heterogeneity, which reveals the risk and bubble in the effect of house market on enterprise financing, and enlightens how to promote financing ability of company.

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

2019 ◽  
Vol 12 (4) ◽  
pp. 746-762 ◽  
Author(s):  
Md Abdullah Al-Masum ◽  
Chyi Lin Lee
Keyword(s):  
Investment Decision ◽  
Housing Prices ◽  
National Level ◽  
Housing Price ◽  
Capital City ◽  
Content Type ◽  
Price Series ◽  
Long Run ◽  
Investment Decision Making ◽  
Market Fundamentals

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.

Green federal tax credits impact on US housing prices

2020 ◽  
Vol 13 (4) ◽  
pp. 553-564
Author(s):  
Billie Ann Brotman
Keyword(s):  
Regression Model ◽  
Energy Policy ◽  
Income Tax ◽  
Housing Prices ◽  
Housing Price ◽  
Tax Credits ◽  
Disposable Income ◽  
Content Type ◽  
Dummy Variable ◽  
Energy Policy Act

Purpose The purpose of this study is to investigate whether increases in homeowner green amenities occurred because of income tax credits to the degree that changes in housing prices are measurable. Are higher incomes, lower mortgage rates and green income-tax credits impacting housing price changes? Design/methodology/approach The paper uses the least-squares regression model with natural log specifications. The log of income and a dummy variable, which was assigned to the Energy Policy Act (2005) and the American Recovery and Reinvestment Act (2009) coverage dates are used as independent variables. Two regression models were examined using monthly housing price data from January 1990 through the year 2018. The first regression model used a single dummy variable for credits available under the Policy Act of 2005 and the Recovery Act of 2009. The second regression model considered the credits granted under these two laws separately. Disposable income per capita impacts demands for housing while green upgrade expenditures affect the cost of housing. Findings The laws set low credit limits of $500 followed by $1,500 but because of the multiplier effect, the spending appears to have magnified and been much higher. The credit availability variables have positive coefficients and were significant at 1 per cent. This implies that single-family housing prices were sensitive to the existence of residential energy property income-tax credits. The R2 results were 0.93 or above for both models. Research limitations/implications The data used was aggregated and publicly available online. Many studies use aggregated macroeconomic data when modeling housing prices using the exogenous variable of disposable income but there is no substitute for examining individual homes by location and their sales price to see under what conditions green income-tax credits have the most impact. There could be demographic issues that are missed when using aggregated information. Practical implications Spending on heating/cooling systems, dual pane windows and other green amenities keeps the housing stock modernized and housing prices steady or rising. An additional benefit is that spending motivated by self-interest can simulate household consumption spending. Houses deteriorate due to wear and tear. Physical-repairable depreciation represents a situation where maintenance funds are continuously needing to be spent. Repairs and upgrades to the structure of the property keep its price stable by stopping the physical depreciation that would otherwise occur with the passage of time. Social implications The paper provides support for the idea that residential green amenity upgrades positively impact the value of a house. These green-amenity upgrades, which other research studies have suggested should be included explicitly in the appraisal process, are a major characteristic of a property when a price estimate is being done. Housing being sold should have a section on the information sheet noting the property green upgrades that exist and an energy efficiency score should be assigned to each house listed for sale. Originality/value There are few (if any) academic research papers studying the impact of green tax credits available under the Energy Policy Act (2005) and under the American Recovery and Reinvestment Act (2009). The degree to which green income-tax credits stimulate spending on housing has not been addressed by researchers. This paper is an initial research attempt to quantify whether these legislative efforts measurably encouraged homeowners to adopt newer, greener technologies.

scholarly journals The effects of tourism on housing prices: applying a difference-in-differences methodology to the Portuguese market

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
António Manuel Cunha ◽  
Júlio Lobão
Keyword(s):  
Real Estate ◽  
Housing Prices ◽  
Housing Price ◽  
Generalized Least Squares ◽  
Equation Model ◽  
Seemingly Unrelated Regression ◽  
Housing Supply ◽  
Difference In Differences ◽  
Content Type ◽  
Price Increases

Purpose This paper aims to explore the effects of a surge in tourism short-term rentals (STR) on housing prices in municipalities within Portugal’s two largest Metropolitan Statistical Areas. Design/methodology/approach This study applies the difference-in-differences (DiD) methodology by using a feasible generalized least squares (FGLS) estimator in a seemingly unrelated regression (SUR) equation model. Findings The results show that the liberalization of STR had a significant impact on housing prices in municipalities where a higher percentage of housing was transferred to tourism. This transfer led to a leftward shift in the housing supply and a consequent increase in housing prices. These price increases are much higher than those found in previous studies on the same subject. The authors also found that municipalities with more STR had low housing elasticities, which indicates that adjustments to the transfer of real estate from housing to tourism were made by increasing house prices, and not by increasing supply quantities. Practical implications The study suggests that an unforeseen consequence of allowing property owners to transfer the use of real estate from housing to other services (namely, tourism) was extreme housing price increases due to inelastic housing supply. Originality/value This is the first time that the DiD methodology has been applied in real estate markets using FGLS in a SUR equation model and the authors show that it produces more precise estimates than the baseline OLS FE. The authors also find evidence of a supply shock provoked by STR.

Impact of macroeconomic indicators on housing prices

2019 ◽  
Vol 12 (6) ◽  
pp. 1055-1071 ◽  
Author(s):  
Satish Mohan ◽  
Alan Hutson ◽  
Ian MacDonald ◽  
Chung Chun Lin
Keyword(s):  
Price Index ◽  
Time Series Data ◽  
Statistical Significance ◽  
Housing Prices ◽  
Consumer Price Index ◽  
New York State ◽  
Housing Price ◽  
Series Data ◽  
Content Type ◽  
Macroeconomic Indicators

Purpose This paper uses statistical analyses to quantify the effects of five major macroeconomic indicators, namely crude oil price, 30-year mortgage interest rate (IR), Consumer Price Index (CPI), Dow Jones Industrial Average (DJIA), and unemployment rate (UR), on housing prices over time. Design/methodology/approach Housing price is measured as housing price index (HPI) and is treated as a variable affecting itself. Actual housing sale prices in the Town of Amherst, New York State, USA, 1999-2008, and time-series data of the macroeconomic indicators, 2000-2017, were used in a vector autoregression statistical model to examine the data that show the greatest statistical significance and exert maximum quantitative effects of macroeconomic indicators on housing prices. Findings The analyses concluded that the 30-year IR and HPI have statistically significant effects on housing prices. IR has the highest effect, contributing 5.0 per cent of variance in the first month to 8.5 per cent in the twelfth. The UR has the next greatest influence followed by DJIA and CPI. The disturbance from HPI itself causes the greatest variability in future prices: up to 92.7 per cent in variance 1 month ahead and approximately 74.5 per cent 12 months ahead. This result indicates that current changes in house prices heavily influence people’s expectation of future prices. The total effect of the error variance of the macroeconomic indicators ranged from 7.3 per cent in the first month to 25.5 per cent in the twelfth. Originality/value The conclusions in this paper, along with related tables and figures, will be useful to the housing and real estate communities in planning their business for the next years.

Long-run drivers and integration in interprovincial Canadian housing price relations

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Vijay Kumar Vishwakarma
Keyword(s):  
Housing Market ◽  
Granger Causality ◽  
Global Financial Crisis ◽  
Housing Prices ◽  
Housing Price ◽  
Housing Markets ◽  
Content Type ◽  
Stochastic Trend ◽  
Long Run ◽  
Long Run Equilibrium

Purpose This paper aims to examine the integration of housing markets in Canada by examining housing price data (1999–2016) of six metropolitan areas in different provinces, namely, Calgary, Vancouver, Winnipeg, Toronto, Montreal and Halifax. The authors test for cointegration, driver cities of long-run relationships, long-run Granger causality and instantaneous causality in light of the global financial crisis (GFC) (2007–2008). Design/methodology/approach The authors use Johansen’s system cointegration approach with structural breaks. Moving average representation is used for common stochastic trend(s) analysis. Finally, the authors apply vector error correction model-based Granger causality and instantaneous causality. Findings Cities’ housing prices are in long-run equilibrium. Post-crisis Canadian housing markets became more integrated. The Calgary, Vancouver, Toronto and Montreal markets drive the Canadian housing market, leading all cities toward long-run equilibrium. Strong long-run Granger causality exists, but the authors observe no instantaneous causality. Price information takes time to disseminate, and long-run price adjustments play a significant role in causation. Practical implications The findings of cointegration increasing after the GFC and strong lead–lag can be used by investors to arbitrage and optimize portfolios. This can also help national and local policymakers in mitigating risk. Incorporating these findings can lead to better price forecasting. Originality/value This study presents many novelties for the Canadian housing market: it is the first to use repeat-sales regional pricing indices to test long-run behaviors, conduct common stochastic trend analyzes and present causality relations.

Research on the Formation Mechanism and Dynamic Factors of Commercial Housing Prices Based on the Correction Carey Model - Panel Data from Thirty-Five Key Cities

2013 ◽  
Vol 291-294 ◽  
pp. 1318-1322
Author(s):  
Feng Lan ◽  
Qian Gu
Keyword(s):  
Panel Data ◽  
Fixed Effects ◽  
Housing Prices ◽  
Housing Price ◽  
Dynamic Factors ◽  
Commercial Housing ◽  
Real Estate Prices ◽  
The Individual ◽  
Two Stages ◽  
Price Analysis

Since 2004, a nationwide commercial housing prices showing explosive growth, This paper use the Carey model studying the volatility of commodity housing price mechanism theory. By Carey theoretical correction model in 1998-2003 and 2004-2010, respectively, in two stages to establish the individual fixed effects and individual random effects panel data model focused on the monitoring of the Ministry of Construction of the 35 cities of the empirical testing of commercial housing price. Analysis concluded that the cost of land, building costs, per capita disposable income of urban residents, urban population, and psychological expectations is the main factors to promote China's real estate prices.

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