Price Appreciation Rates Research Articles

Results of the Far East Mortgage Program for Consumers, Developers, and the Government

The volume of mortgage loans issued in eleven regions of the Far Eastern Federal District at the end of 2023 increased 2.5 times by the start of the Far Eastern Mortgage program in 2020. The most interesting result of the program is an increase in demand for primary real estate and a simultaneous decrease in the affordability of this housing. Since the start of the program, prices have increased by 116% vs. 26% while maintaining historical growth rates. By the end of 2021, the price of an apartment, including interest under the Far East Program, has become higher than that of an apartment while maintaining the historical rate of price appreciation under the family mortgage rate. The program has effectively made housing less affordable. The main conclusion is that for program participants (young families under 35), the most effective benefit of stimulating home buying is lower interest payments, not down payments. The main beneficiaries of rising prices and the scale of lending are developers. Considering the increased price of construction materials, the developers’ margin in the Far Eastern Federal District in 2023 increased to 58%. On the one hand, the government has achieved its goal of boosting the housing market in the Far East and has found an effective tool to encourage young families to buy real estate. This is evidenced by the extension of the program until 2030. On the other hand, several economic shocks led to a significant increase in the key interest rate and the accompanying growth of the program implementation costs.

Transforming homeownership: an innovative financing model with a future value approach

Purpose This study aims to address the critical need for innovative financing solutions in the global housing sector, focusing specifically on Malaysia’s distinct housing finance system encompassing both conventional and Islamic loans. The primary objective is to develop a transformative housing finance model that addresses affordability challenges and reshapes the Malaysian housing landscape. Design/methodology/approach The study presents an alternate housing finance model for Malaysia, integrating lower monthly payments and reduced household debt. Key variables include house price appreciation rates, interest rates, initial guarantee fees and loan-to-value ratios. Inspired by the Help to Buy (HTB) scheme, the model aligns with proven global initiatives for enhanced affordability, balancing payment amounts, loan interest rates and acceptable price thresholds. Findings The study’s findings promise to address affordability disparities and reshape Malaysia’s housing finance landscape. The emphasis is on introducing a structured repayment plan that offers a sustainable path to homeownership, particularly for low-income families. Incorporating the future value adaptation concept, inspired by reverse mortgages and Islamic finance, enhances adaptability, ensuring long-term sustainability despite economic shifts. Practical implications The proposed model promotes widespread access to homeownership, offering practical solutions for policymakers to improve affordability, prompting adaptable risk management strategies for financial institutions and empowering potential homebuyers with increased flexibility. Originality/value The study introduces a transformative housing finance model for Malaysia, merging elements from reverse mortgages, Islamic finance and the HTB scheme, offering potential applicability to similar systems globally.

A Revisit of Supply Elasticity and Within-city Heterogeneity of Housing Price Movements

AbstractWhile supply elasticity can explain why housing prices appreciate by different amounts across cities, it may play a lesser role in smaller geographic units, such as neighbourhoods within a city. This is because of location substitution: a city cannot be easily substituted by another city, but neighbourhoods of the same city can be close substitutes. This paper revisits the question of whether supply elasticity can differentiate housing price appreciation rates within a city by carefully accounting for substitution effects at the neighbourhood level. From a Hong Kong housing boom (2003—2018), we have found that the impact of supply elasticity on another neighbourhood on average is about one-tenth of the impact on its own neighbourhood. It rejects the notion of perfect substitution, under which this magnitude difference should not have been identified. The contribution of this paper is threefold: 1) It clarifies the theoretical relationship between supply elasticity and substitution in shaping housing price movements. 2) It proposes two novel ways to account for neighbourhoods’ substitution using the spatial spillover of land availability and price co-movement. 3) It delivers a clear answer that supply elasticity can shape the housing price movements within a city.

Intermediate Effect of the COVID-19 Pandemic on Prices of Housing near Light Rail Transit: A Case Study of the Portland Metropolitan Area

This study explored the dynamics of a residential property value premium for proximity to a light rail transit (LRT) station in the intermediate term (roughly two years) since the pandemic. We applied a longitudinal quasi-experimental design using repeat sales data from the Portland Metropolitan Area, Oregon. Our results indicate that the effect of the pandemic on prices of housing near LRT stations differs between single-family and multi-family markets. Since the pandemic outbreak, there has been no statically significant difference in the price appreciation between single-family (SF) housing within an LRT service area and otherwise similar SF homes; however, for multi-family (MF) homes, those within an LRT service area have experienced a 3.0% lower price appreciation rate than MFs outside such areas with similar characteristics. Our findings help better highlight the impact of the pandemic on the real estate market and can inform discussions about longer-term changes in post-COVID cities and their planning.

Multiscale analysis of human social sensing of urban appearance and its effects on house price appreciation in Wuhan, China

• Explore the effect of human social sensing of urban appearance on the house price appreciation rate with consideration of temporal and scale heterogeneity. • Use machine learning with big data containing 34 variables. • Provide strategies to achieve sustainable smart urban layout plan. City managers seek to achieve people-oriented sustainable city development, which requires a clear understanding of socioeconomic effects of citizens’ perceptions of urban appearance. Traditional studies have investigated effects of urban appearance but have ignored the perception of place, which depends on people's unique social experiences. We distinguish “house” and “home” and propose an integrated model to test the prediction effect of combining conventional perception and social perception variables on house price appreciation. We establish a dataset that includes factors, e.g., housing structures, visual quality, and human physical perception. Then, we use machine learning models to extract features from multisource data, investigate the price appreciation of 1,032 houses in Wuhan from 2015 to 2020 and use multiscale geographically weighted regression (MGWR) to discuss spatial dependence. Finally, we perform a cluster analysis to understand the combined effects. The results show that human physical perception has the highest effect, the visual quality of urban streets has the highest impact in places where highly educated people gather, and the impact of service facilities is greatest in economically underdeveloped areas. Our findings provide novel insights into the interlinkages between human social sensing and appreciation rates, which can be efficiently applied to build sustainable smart cities.

Tracking the pulse of a city—3D real estate price heat maps

AbstractThis paper proposes the use of a semiparametric model based on a locally weighted approach that controls for dynamic agglomeration and diffusion effects in constructing localized housing price indices. Based on residential transaction records in Singapore, we create three‐dimensional interactive heat maps that allow for better measurement and visualization of spatial variations and heterogeneity in price appreciation. The heat map captures regional variations and temporal dispersions in price appreciation rates in the business cycle. Our methodology provides high‐resolution information about price dynamics that could aid individual buyers, investors, and policy makers in making objective and informed decisions.

Divergence of House Prices in the United States: 2000 to 2015

Housing is the largest component of most American households’ wealth. Divergence of house prices directly affects wealth inequality. Using 5.9 million repeat sales of single-family houses, we find strong evidence that pricier houses had higher price appreciation rates from 2000 to 2015. Across houses, when the log house price increased by one standard deviation, the price appreciation rate increased by 70 to 100 basis points per annum. The superior performance of pricier houses was more pronounced in periods when the housing market performed poorly. We also use a variety of specifications to document detailed temporal variation and non-linearity of the relationship between houses’ price appreciation and their prices.

Understanding house price appreciation using multi-source big geo-data and machine learning

Understanding house price appreciation benefits place-based decision makings and real estate market analyses. Although large amounts of interests have been paid in the house price modeling, limited work has focused on evaluating the price appreciation rate. In this study, we propose a data-fusion framework to examine how well house price appreciation potentials can be predicted by combining multiple data sources. We used data sets including house structural attributes, house photos, locational amenities, street view images, transportation accessibility, visitor patterns, and socioeconomic attributes of neighborhoods to enrich our understanding of the real estate appreciation and its predictive modeling. As a case study, we investigate more than 20,000 houses in the Greater Boston Area, and discuss the spatial dependency of house price appreciations, influential variables and their relationships. In detail, we extract deep features from street view images and house photos using a deep learning model, merging features from multi-source data and modeling house price appreciation using machine learning models and the geographically weighted regression at two spatial scales: fine-scale point level and aggregated neighborhood level. Results show that the house price appreciation rate can be modeled with high accuracy using the proposed framework (R2=0.74 for gradient boosting machine at neighborhood-scale). We discovered that houses with low house prices and small house areas may have a higher house appreciation potential. Our results provide insights into how multi-source big geo-data can be employed in machine learning frameworks to characterize real estate price trends and help understand human settlements for policy-making.

Tracking the Pulse of a City – 3D Real Estate Price Heat Maps

This paper proposes the use of a semiparametric model based on a locally weighted approach that controls for dynamic agglomeration and diffusion effects in constructing localized housing price indices. Based on residential transaction records in Singapore, we create a three-dimensional interactive heat maps that allow for better measurement and visualization of spatial variations and heterogeneity in price appreciation. The heat map captures regional variations and temporal dispersions in price appreciation rates in the business cycle. Our methodology provides high-resolution information about price dynamics that could aid individual buyers, investors, and policy makers in making objective and informed decisions.

Identifying House Price Booms, Bubbles and Busts: A Disequilibrium Analysis from Chaos Theory

This research was motivated by the excesses of public policy since 2008 in an attempt to re-inflate the housing markets. Is it even possible or desirable to utilize such a vast amount of public resources to inflate a single sector such as housing that suffered from such a spectacular bubble and collapse? The consequences suggest that, as a way to bolster real household incomes and aggregate output, these policies have disappointed. In contrast, there is a fear that the monetary stimuli will lead to unsustainable housing price inflation, if not a bubble. I address these questions in the analysis from the standpoint of determining the stable equilibrium and sustainable house price appreciation rates consistent with the growth of median household income. The problem of identifying stable house price appreciation is to first identify the major proximate determinants of household demand for housing. A second is to show empirically the movement, deviation, and variation of these factors over time compared to housing prices. I use median household income as the major demand factor for houses and median single family house prices as an indicator of the price. A third is determining the stable equilibrium of the growth of these factors and the appreciation of housing prices consistent with them. And a fourth is the adjustment process when there are small deviations from steady-state equilibrium compared to when deviations are large. It is this last distinction where the chaos theory of self-organizing systems and irreversibility of the housing market system enters to explain how the adjustment process is chaotic in this case. I conclude that, as of the beginning of June 2016, the evidence is overwhelming that housing price appreciation is in a bubble that will likely lead to significant declines in house price appreciation if not in house prices. An important policy recommendation to mitigate these declines and hasten a house price recovery follows. The continuation of expansionary monetary policies will only delay house price adjustments and lead to more severe price declines.

Inefficiency of Korean Apartment and Jeonse Market : Focused on Jeonse-to-price Ratio

According to a well-known arbitrage equation between house price and Jeonse (key money lump-sum deposit on a rental) price in Korea, when a house price is expected to rise or interest rate falls, the Jeonse-to-price ratio falls. However, in the actual housing market, when prices rise or interest rate falls, the ratio rises for a while. This study examined this process with TARCH and Markov switching analysis. The analysis shows that the growth and disappearance of expected excess returns could well explain the process of the ratio. In particular, the effect of changes in the expected apartment price appreciation rate and the mortgage rates on the ratio might differ in the low volatility and high volatility regimes of expected excess returns. Based on these results, if the housing market stabilizes, the expected excess returns accumulated in the apartment Jeonse market will gradually decrease. In addition, if the current ultra-low interest rates are resolved, the process will be strengthened. It would enhance the efficiency of the housing market, and the Jeonse market would look for relative stability.

Housing Consumption and Investment: Evidence from Shared Equity Mortgages

Academics have proposed hybrid products with equity features for the financing of housing. In spite of their risk-sharing benefits these products have not become mainstream. This paper studies an important exception, a UK government scheme which in the five years since its inception has provided almost £10 billion of equity financing. The analysis of the origination and prepayment behavior of households who have used the scheme highlights housing affordability constraints. A difference-in-difference analysis of an increase in the maximum government equity limit shows that households took advantage of the increase to buy more expensive properties, and not to reduce their mortgage debt and house price risk exposure. A counterfactual study of homebuyers who, instead of using the equity available, relied on high loan-to-value mortgages shows that their financing choices can be rationalized by an expected rate of house price appreciation of 7.7% per year. We draw general implications for how households approach their house purchase and financing decisions, taking advantage of the fact that the shared equity mortgages that we study allow the separation of the consumption and investment dimensions of housing.

Has the Effect of Housing Wealth on Household Consumption Been Overestimated? New Evidences on Magnitude and Allocation

The effect of housing wealth on household consumption is puzzling as existing empirical results do not match with theoretical predictions. Existing theories – life cycle theory, permanent income hypothesis and user cost model – suggest that housing wealth impact should be small. However, most prior studies find that Marginal Propensity to Consume (MPC) out of housing wealth ranges between 0.04 to 0.09, indicating material impact of housing wealth on household consumption. Motivated by this discordance, this study uses the Panel Study of Income Dynamics to provide a step by step analysis to show how the housing wealth effect decreases as biases from unobserved variables are properly addressed. Once households’ unobserved preferences towards consumption and their future expected income are controlled for, our estimated MPC drops significantly. We also directly control for home equity extraction to disentangle the pure wealth effect channel and the collateral channel. Our findings show that a one percent increase in perceived housing wealth is associated with 0.01-0.02 percent increase in real, non-housing consumption after directly controlling for the collateral channel. Our estimated magnitude of housing wealth effect is much smaller than previous findings. Additionally, we find heterogeneity in MPCs across consumption categories – consumptions that are necessary in daily lives such as food and transportation do not respond to changes in perceived housing wealth while households increase their spending on clothes and recreation as housing wealth increases. We also employ an IV approach to disentangle permanent and transitory housing wealth shocks. Our results indicate that it is the deviation between perceived house price appreciation rate and the real house price appreciation rate in fundamental values that drives this small magnitude of MPC out of housing wealth in the short run (in cloth and recreation) and this housing wealth effect will move towards to zero in the long run as the perception converges with the fundamental values.

주택임대시장 균형조건을 이용한 주택매매가격 대비 전세가격 비율 결정요인 분석: 주택매매가격 변동성을 중심으로

Traditional approach to analyze the determinants of Chonsei deposit to housing sales price ratio is based on asset market equilibrium condition. However, this approach does not fully account for complexity involved in Korean rental housing market, where Chonsei, Monthly-rent-with-variable deposit, and pure monthly rental contracts coexist. In this paper, we utilize each rental type’s different rate of return structure and examine the determinants of Chonsei deposit to housing sales price ratio based on rental housing market equilibrium condition. We further incorporate housing price volatility into the analysis as a critical variable related to housing investment risks, which has been ignored in previous studies. Our empirical analysis using a sample of the apartment sales price and rent in Seoul confirm our theoretical predictions: Chonsei deposit to housing sales price ratio is positively linked to the rent-to-price ratio and housing price volatility, and the ratio is negatively affected by the expected housing price appreciation rate. The relationship between the Chonsei deposit to housing sales price ratio and housing price volatility was robust to different measures of housing price volatility. The long-run elasticity of Chonsei deposit to housing sales price ratio with respect to rentto- price ratio was estimated to be 1.04~1.22 and the long-run elasticity of the ratio with respect to housing price volatility was estimated to be 0.15~0.53. which is about 50 to 100 percent of the long-run elasticity with respect to expected housing price appreciation rate. These results show that previously ignored housing price volatility is one of important variables to affect Chonsei deposit to housing sales price ratio and risk factors are needed to be considered to better understand Korean housing market.

Climate gentrification: from theory to empiricism in Miami-Dade County, Florida

This article provides a conceptual model for the pathways by which climate change could operate to impact geographies and property markets whose inferior or superior qualities for supporting the built environment are subject to a descriptive theory known as ‘Climate Gentrification.’ The article utilizes Miami-Dade County, Florida (MDC) as a case study to explore the market mechanisms that speak to the operations and processes inherent in the theory. This article tests the hypothesis that the rate of price appreciation of single-family properties in MDC is positively related to and correlated with incremental measures of higher elevation (the ‘Elevation Hypothesis’). As a reflection of an increase in observed nuisance flooding and relative SLR, the second hypothesis is that the rates of price appreciation in lowest the elevation cohorts have not kept up with the rates of appreciation of higher elevation cohorts since approximately 2000 (the ‘Nuisance Hypothesis’). The findings support a validation of both hypotheses and suggest the potential existence of consumer preferences that are based, in part, on perceptions of flood risk and/or observations of flooding. These preferences and perceptions are anticipated to be amplified by climate change in a manner that reinforces the proposition that climate change impacts will affect the marketability and valuation of property with varying degrees of environmental exposure and resilience functionality. Uncovering these empirical relationships is a critical first step for understanding the occurrence and parameters of Climate Gentrification.

Identifying House Price Booms, Bubbles and Busts: A Disequilibrium Analysis from Chaos Theory

This research has been motivated by the excesses of public policy since 2008 in an attempt to re-inflate the housing markets. Is it even possible or desirable to utilize such a vast amount of public resources to inflate a single sector such as housing that suffered from such a spectacular bubble and collapse? The consequences suggest that, as a way to bolster real household incomes and aggregate output, these policies have disappointed. In contrast, there is a fear that the monetary stimuli will lead to an unsustainable housing price inflation, if not a bubble. I address these questions in the analysis from the standpoint of determining the stable equilibrium and sustainable house price appreciation rates consistent with the growth of median household income. The problem of identifying stable house price appreciation is to first identify the major proximate determinants of household demand for housing. A second is to show empirically the movement, deviation, and variation of these factors over time compared to housing prices. I use median household income as the major demand factor for houses and median single family house prices as an indicator of the price. A third is determining the stable equilibrium of the growth of these factors and the appreciation of housing prices consistent with them. And a fourth is the adjustment process when there are small deviations from steady-state equilibrium compared to when deviations are large. It is this last distinction where the chaos theory of self-organizing systems and irreversibility of the housing market system enters to explain how the adjustment process is chaotic in this case. I conclude that, as of the beginning of June 2016, the evidence is overwhelming that housing price appreciation is in a bubble that will likely lead to significant declines in house price appreciation if not in house prices. An important policy recommendation to mitigate these declines and hasten a house price recovery follows. The continuation of expansionary monetary policies will only delay house price adjustments and lead to more severe price declines.

The effect of local amenities on house price appreciation amid market shocks: The case of school quality

Combining the strengths of price index and boundary fixed effects models, this paper examines the factors leading to spatial house price appreciation divergences during market shocks. Specifically, I focus on the relationship between school quality and house price appreciation rates during the period of rapid house price increases and decreases between 2000 and 2012. I find that appreciation rates diverge across school districts in Franklin County, OH during the entire time period. Results identified through spatial-temporal boundary controls provide evidence that standardized test scores positively drive this relationship during the housing market decline in the latter half of the period from 2007 to 2012; in contrast, there is no overall relationship between school quality and appreciation rates during the preceding period of housing price increases from 2000 to 2006.

Variation Across Price Segments and Locations: A Comprehensive Quantile Regression Analysis of the Sydney Housing Market

AbstractStandard house price indices measure average movements of average houses in average locations belonging to an average price segment and hence obscure spatial and cross‐sectional variation of price appreciation rates even within a single metropolitan area. This article combines penalized quantile regression techniques with the hedonic imputation approach to reveal such kind of variation. The method is applied to house transactions from Sydney between 2001 and 2014. The analysis finds significant variation across sub‐markets over time and in particular during the boom‐and‐bust cycle peaking in 2004. Appreciation rates were highest for suburban, low‐priced and lowest for inner‐city, high‐priced houses.

Information Frictions and Housing Market Dynamics

This paper examines the effects of seller uncertainty over their home value on the housing market. Using evidence from a new dataset on home listings and transactions, I first show that sellers do not have full information about current period demand conditions for their homes. I incorporate this type of uncertainty into a dynamic search model of the home selling problem with Bayesian learning. Simulations of the estimated model show that information frictions help explain short-run persistence in price appreciation rates and a positive (negative) correlation between price changes and sales volume (time on market).

INFORMATION FRICTIONS AND HOUSING MARKET DYNAMICS

I examine the effects of seller uncertainty over their home value on the housing market. Using evidence from home listings and transactions data, I first show that sellers do not have full information about current period demand conditions for their homes. I incorporate this type of uncertainty into a dynamic microsearch model of the home selling problem with Bayesian learning. The estimated model highlights how information frictions help to explain the microdecisions of sellers and how these microdecisions affect aggregate market dynamics. The model generates a significant microfounded momentum effect in short‐run aggregate price appreciation rates.