In markets dominated by pre-sales, which is a structural feature of housing provision in emerging economies, constructing price indices is limited by the lack of resale history. Addressing this statistical blind spot, this study proposes a novel empirical strategy which exploitspurchase contract cancellations to identify repeat-sales pairs. Utilizing a dataset of 25,049 pairs from Santiago (2013-2024)in Chile,this approach mitigates depreciation bias by comparing the same never-occupied unit over time. Results from geometric and arithmetic estimators reveal three key findings: (1) a distinct price hierarchy, where capital appreciation disproportionately favorsentry-levelhousingand investment segments; (2) the index serves as a leading indicator, which anticipated the post-2021 market correction ahead of official administrative records; and (3) a 27% nominal loss rate in subsequent placements, which exposes the magnitude of price adjustments consistent with liquidity frictions. These findings validate cancellations not merely as administrative voids, but as real-time signals of financial distress, thus offeringa replicable monitoring tool for global jurisdictions reliant on the pre-sale leverage model.
The Taiwanese government has long regarded the real estate sector a a key driver of economic growth because of its perceived multiplie effects. However, rising housing prices and worsening affordability hav raised concerns about whether the housing market contribute meaningfully to long-term economic development. This study re examines the relationship between housing prices and the macroeconomy in Taiwan by using quarterly data from 2006Q1 t 2025Q3 within a vector error correction model framework. The result confirm the existence of long-run cointegration among housing prices gross domestic product (GDP), construction activity, population, an inflation. Housing prices are found to be weakly exogenous, while GDF construction activity, population, and inflation adjust to restor equilibrium following shocks. In the long run, housing prices ar influenced by supply-side conditions, demand pressures, financia factors, and structural shocks such as the global financial crisis an COVID-19 pandemic. In the short run, housing price shocks exert limite effects on broader economic performance. Overall, the findings sugges that although the real estate sector is closely linked to macroeconomi conditions, its contribution to sustained economic growth is limited.
This study examines how board structure, director characteristics, and firm fundamentals jointly influence the frequency of meetings of real estate investment trust (REITs) boards from 2010 to 2022. We find that board activity is largely reactive to firm conditions rather than a proactive governance mechanism. Across the full sample, the boards meet more frequently following a weaker performance, higher leverage, and greater uncertainty, which is consistent with meetings that serveas monitoring responses to emerging risks. We further document that firms with weaker growth prospects shift board attention toward monitoring, while firms with stronger growth opportunities engage boards more in advisory and strategic deliberations. In addition, there is substantial heterogeneity across institutional environments. In large, highly visible S&P 500 REITs, board size and independence are associated with greater engagement across monitoring and advisory functions, thus suggesting that governance structures operate more effectively under strong market scrutiny and transparent information environments. In contrast, smaller non-S&P 500 REITs exhibit more episodic governance patterns in which meeting activity responds broadly to performance shocksand financial risk, and where structural governance attributes do not consistently translate into board engagement. Additional analyses show that board meetings respond to prior firm conditions but do not predict future operating performance, thus indicating that meeting frequency signals governance response rather than governance effectiveness
This paper presents an improved model for forecasting rental property values in large metropolitan areas by incorporating features derived from the spatial distribution of urban lighting. The study uses rental housing data from Houston and Los Angeles (USA) together with high-resolution nighttime satellite imagery to generate additional explanatory variables. Light clusters are identified from satellite images and processed to determine their geographic location and spatial relationships. The clusters are georeferenced by using the Quantum Geographic Information System,thusenabling integration with other spatial datasets and improving modelling accuracy. The paper describes the methodology for feature extraction, spatial clustering, and integration into machine learning workflows. A Light Gradient-Boosting Machine predictive model is developed and compared with baseline models. The experimental results show that the proposed approach reduces the mean squared error by 11.8% for Houston and 9.37% for Los Angeles relative to conventional models. The findings demonstrate the usefulness of nighttime illumination features for capturing socio-economic and spatial patterns relevant to urban rental markets. The proposed methodology highlights the potential of combining geospatial data and machine learning techniques to improve automated valuation models and support urban analytics and smart city planning.
The built environment accounts for a substantial share of urban greenhouse gas emissions, and a growing number of U.S. cities have responded by enacting mandatory building performance standards (BPS) that impose energy and emissions targets on commercial and multifamily properties. Multifamily housing, which represents roughly one-third of the U.S. housing stock and an even larger share of building-sector emissions in major cities, sits at the center of this regulatory shift. However, the BPS landscape is highly fragmented across jurisdictions in terms of metrics, targets, and enforcement, thus creating compliance complexity for developers, owners, and asset managers. This report draws on interviews with executives ofrepresentative multifamily companies across the United States to document industry perceptions and experiences of municipal energy efficiency mandates. It summarizes the principal compliance challenges, cost and operational implications, and strategic considerations that shapehow the multifamily sector is navigating the transition to stricter performance standards.
This study investigates the impact of a government-borne value added tax (VAT) incentive on residential property demand across Indonesian provinces from 2018 to 2023. Introduced as part of the post-pandemic recovery efforts, the policy aimed to stimulate housing demand amid declining market activity. Using provincial panel data, the analysis employs an interrupted time series approach complemented by a dynamic panel regression to capture both the immediate and delayed effects of the fiscal intervention while controlling for income, unemployment, and property prices. The findings reveal a two-stage response: an initial contraction in demand following policy implementation, thereby reflecting short-term market rigidity, followed by a sustained upward trend as economic confidence improves. The positive effect is more substantial in Java provinces, thus suggesting that regional economic structures and financial depth shape policy responsiveness. Overall, the results confirm that VAT incentives effectively bolster residential property demand and function as an important fiscal lever for stabilizing cyclical downturns. The study highlights that VAT incentives should not be viewed solely as crisismarket conditions weaken.
The operating decisions of investors can be irrational. This research proposes an approach to measure overconfidence bias and the disposition effect based on real estate rental decisions, and disentangle from market friction. Irrational operating decisions transform into impacts on real estate rentable supply responsiveness and market illiquidity. Analyzing US office market data from 2005 to 2019, the empirical findings confirm that the disposition effect significantly impacts supply responsiveness, alongside the effects of overconfidence, regulations and geographical barriers. Friction is the leading cause of market illiquidity, and the level of market illiquidity due to the disposition effect is higher than the overconfidence bias; thus, the disposition effect more frequently occurs.
Real estate agents provide unstructured information in marketing remarks for listed housing transactions that potential buyers may find valuable; however, research on their impact on housing outcomes is limited. This study provides empirical evidence on the impact of unstructured marketing remarks related to green housing features, specifically solar panels, on house prices and the number of days on the market. The results show that remarks about solar panels and financing options have a significant influence on prices and time on the market. After controlling for potential selection bias, our results indicate that properties including marketing comments related to owned, leased, or Power Purchase Agreement solar panels positively capitalize into higher selling prices, yet mentioning the presence of solar panels without mentioning the type of financing negatively impacts property prices. Other green attributes highlighted in marketing remarks show mixed price effects.
Unreliable property price information can lead to misallocation of land use and inefficient utilization of scarce resources. Traditional hedonic pricing models estimated by using ordinary least squares (OLS) commonly include property age as an explanatory variable, which implicitly assumes that both land and building structures depreciate over time. However, recent research argues that residential property price indices (RPPIs) should separate the depreciation of the building structure from the land component. Under this view, property age should only interact with the structural characteristics of the building, not the land. This paper estimates RPPIs by using both a standard time dummy hedonic price model and a modified version that adjusts for structural quality by interacting age with building features. Using a dataset of 451,894 observations, we find that the standard model tends to produce a downward bias in periods of mild price variation and an upward bias estimated indices with those published by the local government. The results show that turning points, price peaks, and troughs are largely consistent across both sets of indices.
In this study, we examine the effect of regional innovation with the number of registered patents as a proxy for innovation on regional growth in South Korea. We use economic and housing price data from 17 regions from 2012 to 2021 to examine the interrelationships among housing market and local innovation and regional growth. We adopt the 2-stage least squares method to overcome the endogeneity issue of our regional growth and housing price models while controlling for housing price appreciation and regional specific characteristics. Our findings reveal that while innovation has a significantly positive effect on both the local residential real estate market and regional growth, housing price does not affect regional innovation. We find that a 1% increase in patents increases the gross regional domestic product per capita by 0.188%. Innovation has a positive impact on the housing market, with a 10% increase in patent increases leading to an increase of 0.329% in housing prices. Our findings have a policy implication: innovative activity stimulates regional growth and innovation driven growth increases housing demand, thus emphasizing the need to ensure housing affordability and adequate supply.
This study develops a comprehensive machine learning (ML) framework for house price prediction in Vietnam by utilizing a dataset of 28,156 property listings from a real estate website. We employ rigorous data preprocessing, feature engineering, and comparative analysis of ML algorithms, including CatBoost, XGBoost, and random forests. The results demonstrate the superiority of ensemble methods, with CatBoost achieving the highest performance on the main dataset (R2 = 0.510, RMSE = 17.614). Regional analyses in Hanoi and Ho Chi Minh City reveal the adaptability of the models for local market dynamics. A Shapley additive explanations analysis reveals key drivers of house prices, such as area, population density, and property-specific attributes. The findings contribute to the academic understanding of real estate valuation and provide actionable insights for policymakers, investors, and other stakeholders. This study lays the groundwork for developing automated valuation models and their practical implementation, exemplified by a website application. By harnessing ML and data-driven insights, this research advances transparent, efficient, and informed decision-making in the real estate sector in Vietnam, while offering a robust methodology for house price prediction in emerging markets.
After the Industrial Revolution in Europe in the 18th century, the economic development of Asia was initiated by Japan in the 1900s. As the costs of labor and land escalated, some industries gradually relocated to other Asian countries or regions which Akamatsu (1962) terms the flying geese pattern. To explore the flying geese effect between Japan and Taiwan, this study analyzes data from the gross domestic product and stock and housing markets of both countries from 1975 to 2023. During the pre-bubble and the overall study periods, the Japanese markets significantly influenced the stock and housing markets of Taiwan, thus demonstrating the flying geese effect and reflecting the strong economic performance of Japan. However, in the post-bubble period, the Taiwanese markets diverged from the trajectory of Japan, and developed their independent momentum. These shifts can be attributed to the outward capital and industrial migration of Japan, increasing competition from the emerging markets, and growth of the integrated circuit industry of Taiwan.
The underpricing of initial public offerings (IPOs) remains a significant puzzle in the finance literature. While international studies have documented the underpricing anomaly in real estate investment trust (REIT) IPOs, the Chinese REIT (C-REIT) market, now the second largest globally, has received limited attention regarding IPO initial returns. This paper addresses this gap by examining the initial price performance of C-REIT IPOs by using first-day returns and the capital asset pricing model (CAPM). The study further investigates differences in initial returns across various investor types, asset classes, and market development phases. Consistent with global trends, C-REIT IPOs exhibit underpricing, with a mean first-day return of 7.51%. Returns tend to decline after the first day, improving in only 30% of cases by Day 5. The market experienced significant underpricing from 2021 to 2022 but showed recovery in 2024. A regression analysis indicates that issuance size and time to listing are negatively correlated with underpricing, while subscription multiples and performance clauses are positively correlated. The results support the information asymmetry explanation for underpricing. Recommendations are provided for investors and regulators to enhance market efficiency and stability.
Machine learning (ML) methods, such as long short-term memory (LSTM) models, are increasingly proposed as alternatives to traditional statistical approaches for time series forecasting. However, given the speed of the real estate industry in providing data that reflect economic climates, there are few comparisons of ML techniques with statistical methods in the context of real estate data during crisis periods. The study investigates the predictive accuracy of the autoregressive integrated moving average (ARIMA) and LSTM models by using daily data from the Financial Times Stock Exchange/Johannesburg Stock Exchange South Africa Listed Property Index. Through a comprehensive analysis of 1628 observations from January 2, 2015, to July 8, 2021, the study finds that the ARIMA models produce fewer forecasting errors compared to the LSTM models during the COVID-19 crisis. These findings suggest that traditional ARIMA models may be more efficient for forecasting volatile real estate data in crisis periods, although the results could vary with larger and more complex datasets. This research is crucial as it provides insights into the comparative performance of statistical and ML models, thus emphasizing the need for context-specific model selection in economic forecasting.
The Japanese real estate investment trust (J-REIT) market is the second-largest REIT market globally and has played a key role in property acquisitions in Japan over the past two decades. This study examines pricing efficiency in J-REIT acquisitions over a period of 20 years and focuses on whether properties are acquired at underpriced or overpriced amounts relative to fair values and how investors reacted. This study finds that J-REITs acquire the most properties below fair value, with significant variation across types of assets and sellers. Notably, acquisitions from related parties tend to be less underpriced as opposed to those from third parties. This study also assesses investor reactions to underpriced acquisitions by analyzing the cumulative abnormal returns of J-REIT investment unit prices around acquisition announcements with seasoned equity offerings. Our findings indicate that investors are not highly attentive to differences between acquisition prices and fair values, as they do not react to the amount of underpriced acquisitions. This study contributes to understanding valuation dynamics in J-REIT acquisitions and offers insight into investor sentiment regarding pricing efficiency. The work underscores the importance of assessing the fairness and strategic implications of property acquisitions.
The period between 2013 and 2023 was extraordinary for the economy of T & uuml;rkiye. The citizens felt that all of the negativities resulted from the 2023 Kahramanmaras earthquake, the attempt of a military coup in 2016 and COVID-19 pandemic. During these events, sustaining economic policies with healthy dynamics is not easy in terms of micro, macro and international dimensions. It is indispensable for multi-layered real estate markets to exist normally in such unpredictable times. This research mainly focuses on these situations and investigates the relationship between the macroeconomic variables and housing price index changes, which has been accepted as an important variable of price in the real estate market as a price indicator. Using the Markov switching regression model, it is found that inflation and unemployment impact the housing price index changes. On the other hand, another important finding of this research work is the statistical, negative and positive-sided (asymmetric) relationship between gross domestic product growth and housing price index changes. It can be concluded from these findings that the government, investors and consumers of the real estate markets have been subjected to challenging and difficult macro-periods; these extraordinary events have also been so destructive that the expectations, wishes and desires of the market participants are not easily met.
This study examines the potential linkages between corporate public listing activities and performance of local residential mortgage markets with the use of a dataset of 1,100 initial public offerings (IPOs) in the United States (U.S.) from 2000 to 2018. While the existing literature suggests that IPOs may generate positive spillover effects, such as stimulating local businesses and housing markets, we find an unexpected negative correlation between long-term IPO activity and the average performance (particularly foreclosure rates and 90-day delinquency rates) of local mortgage loans. We explore several potential explanations for this relationship and find little evidence to support the hypothesis that it is driven by the post-IPO rising housing costs, exit of wealthier borrowers from the mortgage market due to welfare changes, or cashing out of home equity by local residents to finance their increased stock market participation. However, we do find that IPO activity is positively associated with the local loan-to-household ratio and median original loan-to-value (OLTV) ratio. Additionally, the negative correlation between IPO size and loan performance is stronger when excluding metropolitan statistical areas (MSAs) that are home to the headquarters of the largest mortgage lenders with nationwide operations. The relationship remains after we control for degree of banking restrictions on household loans. Our findings suggest a potential counter-cyclical shift in lending quality, similar to trends identified in the banking literature, where lenders may relax lending standards or reduce the quality of borrower assessments during business upswings following IPOs.
The effects of financialisation on the investment behaviour of nonfinancial firms have become the subject matter of some recent studies. Another strand of the literature focuses on the implications of sector-specific (particularly the housing sector) financialisation. This study combines these two strands of literature by estimating the impact of financialisation on the investment behaviour of a panel of real estate firms in Malaysia, Thailand and the Philippines. The study extends the current knowledge of this subject area by enabling a more micro-level analysis of real estate firm behaviour that uses accounting data, while also drawing important observations about the similarities and differences in how real estate firms in various countries respond to financialisation. Our main findings can be summarised as follows. First, financialisation has a negative effect on the investment behaviour of real estate firms in Malaysia and the Philippines, but not Thailand. Second, past investment decisions, profitability and sales performance tend to reinforce current investment behaviour. Third, increased past leverage discourages investments. The negative impact of financialisation on investment in Malaysia and the Philippines could imply that more financialisation is associated with a tendency to reduce investments in construction activities in these countries. Some recommendations for policy are proposed.
In Singapore, most land is state-owned, with the state generally issuing leasehold estates via state leases of not more than 99 years1, depending on the intended land use. Naturally, the value of a leasehold estate, which erodes over time as the lease approaches the end of its term, is a key component of the premium charged for lease renewals, or the tax imposed for permission given in relation to a development that would increase the value of the land. By law, the state valuation of leasehold land is prescribed by a leasehold relativity table colloquially known as 'Bala's Curve' or 'Bala's Table'. Since its adoption in 1948, however, the underlying assumptions and discount rate inherent to the curve have not been disclosed. This paper aims to deconstruct or reverse engineer Bala's Table to derive the best fit model of the curve. Doing so allows policymakers to evaluate whether the model parameters align with prevailing economic realities, and if not, modify them to reflect the market and more accurately value leasehold estates for calculating taxes and premiums.
