Paper analyzes how interest rates and population growth affect housing prices in China.
problem Effects of interest rate and population growth shocks on housing prices in China.
method Developed a model integrating Lastrapes (2002) and Mankiw & Weil (1989) to simulate housing tax policies.
result Housing tax policies should be tailored to the type of shock affecting housing prices.
Modeling house prices in Australia reveals supply limitations as the primary driver of extreme trends.
problem Understanding the resilience of Australia's housing prices despite changes in mortgage rates.
method Developed a differential equation model and used modern extreme value techniques on real-world data.
result Without supply increases, a 11% mortgage rate hike is needed to moderate extreme housing costs.
XGBoost outperforms other models in predicting housing prices.
problem Accurate housing price prediction for socio-economic development.
method Employed XGBoost and other machine learning algorithms on housing price datasets.
result XGBoost outperformed other models in predicting housing prices.
This paper models London's real estate market trends and identifies key factors influencing house prices.
problem Complex factors influencing London's real estate prices are not well understood.
method Developed a housing price model using principal components analysis to handle multicollinearity.
result Identified the most important factor affecting house prices per square meter.
GSNE improves house price predictions by embedding geo-spatial context.
problem Lack of contextual information in house price prediction models.
method Geo-Spatial Network Embedding (GSNE) using graph neural networks.
result GSNE embeddings consistently improve house price prediction performance.
This paper investigates the risk-return relationship in determination of housing asset pricing. In so doing, the paper evaluates behavioral hypotheses advanced by Case and Shiller (1988, 2002, 2009) in studies of boom and post-boom housing markets. The paper specifies and tests a multi-factor housing asset pricing mode…
This paper explores integration and contagion among US metropolitan housing markets. The analysis applies Federal Housing Finance Agency (FHFA) house price repeat sales indexes from 384 metropolitan areas to estimate a multi-factor model of U.S. housing market integration. It then identifies statistical jumps in metrop…
Satellite imagery improves house price prediction models.
problem Improving accuracy of housing price estimation models.
method Transfer learning from ImageNet-pretrained Inception-v3 model to satellite images.
result Achieved a 10% improvement in R-squared score.
Singapore's cooling measures did not increase housing wealth overall.
problem The impact of cooling measures on housing wealth distribution.
method Examined Singapore's cooling measures over ten rounds, analyzing welfare from housing wealth.
result Welfare from housing wealth in the last round might not be higher than before 2009, depending on the deflator.
Paper predicts house prices in major Chinese cities using LSTM networks.
problem Predicting house prices in major Chinese cities.
method Used LSTM networks compared to ARIMA for prediction accuracy.
result LSTM networks showed superior accuracy in predicting house prices.
The latest global financial tsunami and its follow-up global economic recession has uncovered the crucial impact of housing markets on financial and economic systems. The Chinese stock market experienced a markedly fall during the global financial tsunami and China's economy has also slowed down by about 2\%-3\% when m…
New measure predicts Dutch housing market downturns.
problem Understanding causes of Dutch housing boom and bust.
method Modelled household lending capacity using bank formulas.
result New measure outperforms traditional measures in forecasting housing prices.
The study models mortgage prepayment risk using stochastic housing market activity.
problem Modeling prepayment risk in mortgages under varying housing market conditions.
method Developed a stochastic model for prepayment option value, using swaption pricing formulas and non-standard actuarial hedging.
result Housing market covariance significantly impacts prepayment option prices.
Paper proposes a new hybrid model for forecasting house prices.
problem Forecasting sudden house price drops to prevent financial crises.
method Combines EEMD signal processing with SVR machine learning.
result Proposed model outperforms other models with half the error.
Spatially weighted conformal prediction improves uncertainty quantification in house price models.
problem Uncertainty quantification in automated valuation models with spatial dependencies.
method Survey and demonstration of various spatially weighted approaches to adjust conformal prediction confidence sets.
result Spatially weighted CP makes confidence sets more consistently calibrated across geographical regions.
I studied the convergence of regional house prices to national prices in USA by analyzing time-series of house price indices of 9 Census Divisions. I found the evidence of the convergence in some parts of the country using asymmetric unit root tests. The fact that the evidence of the convergence is not present in large…
Machine learning identifies underpriced homes for investors.
problem Identifying houses listed below market price for quick sale.
method Feature engineering and machine learning algorithms (regression trees, k-nearest neighbors, SVM, neural networks).
result High predictive performance in estimating market prices.
Study shows house buyers in Christchurch value earthquake risk differently based on time since 2011 quake.
problem Understanding how house buyers' perception of earthquake risk changes over time.
method Used a hedonic price model to analyze house prices in Christchurch over three periods.
result Buyers value earthquake risk differently based on the time since the 2011 Christchurch earthquake.
The paper introduces mortgage-rate-adjusted home prices to help buyers and adjust housing indices.
problem Impact of mortgage rates on home prices and property purchase decisions.
method Derives mortgage-rate-adjusted 'effective price' and constructs a price-mortgage rate neutrality line.
result Mortgage rates significantly affect home prices over long periods but not during the pandemic.
Study shows how algorithmic prediction affects US housing market, reducing racial wealth disparities.
problem Impact of algorithmic prediction on housing market and racial wealth disparities.
method Natural experiment using digitization of housing records to study entry, allocation, and prices.
result Digitization leads to increased sale prices for minority-owned homes, reducing racial wealth disparities.
Model explains herding and volatility in urban housing prices.
problem Understanding non-linear price dynamics in urban housing markets.
method Agent-based model with rational households and trend-following behavior.
result Model accurately predicts price variability and herding behavior.
Study shows houses appreciated more during pandemic due to speculation, not just price uncertainty.
problem Impact of COVID-19 on house prices and speculation.
method Quasi-experimental design, unit-level matching, multivariate difference-in-difference regression.
result Properties listed for sale appreciated an additional 1% per month after pandemic onset, with an excess annual growth of 12.7 percentage points.
Understanding how housing values evolve over time is important to policy makers, consumers and real estate professionals. Existing methods for constructing housing indices are computed at a coarse spatial granularity, such as metropolitan regions, which can mask or distort price dynamics apparent in local markets, such…
In this article, we develop a model for the evolution of real estate prices. A wide range of inputs, including stochastic interest rates and changing demands for the asset, are considered. Maximizing their expected utility, home owners make optimal sale decisions given these changing market conditions. Using these opti…
Machine learning models predict housing prices using macroeconomic factors.
problem Predicting housing prices using macroeconomic data.
method Used machine learning (kNN and tree-bagging) on a dataset of macroeconomic factors.
result Machine learning models can predict housing prices with uncertainties better than existing index uncertainties.
The paper uses graph learning to detect valid instruments in high-dimensional data for house pricing.
problem Endogeneity bias and invalid instrument validation in high-dimensional data.
method Merge variable selection algorithms and probabilistic graphs to estimate house prices and causal structure.
result Efficient data-driven instrument selection and invalid instrument purge in high-dimensional data.
New mortgage contracts reduce underwater default by adjusting loan balances, but must balance prepayment incentives.
problem Underwater default incentives in mortgages.
method Analyzes automatic balance adjustment and prepayment penalties in mortgage contracts.
result Automatic balance adjustments are preferable to traditional contracts at certain spreads, reducing underwater default.
A new house price prediction model using location data and multi-task learning.
problem Accurate house price prediction for various stakeholders.
method Location-centered data profiling and Multi-Task Learning (MTL) approach.
result MTL-based methods significantly outperform state-of-the-art approaches in house price prediction.
Housing markets play a crucial role in economies and the collapse of a real-estate bubble usually destabilizes the financial system and causes economic recessions. We investigate the systemic risk and spatiotemporal dynamics of the US housing market (1975-2011) at the state level based on the Random Matrix Theory (RMT)…
Study finds no consistent return predictability using payout ratios across 16 countries.
problem Return predictability using payout ratios in various markets.
method Analysis of 16 developed countries' bond, equity, and housing markets using payout-price ratios.
result No consistent in-sample and out-of-sample performance with positive utility gain.
We propose a simple probabilistic model to explain the spatial structure of the rent distribution of housing market in city of Sapporo. Here we modify the mathematical model proposed by Gauvin et. al. Especially, we consider the competition between two distances, namely, the distance between house and center, and the d…
The paper analyzes potential housing bubbles in China using statistical tests.
problem Detecting potential housing bubbles in China's real estate market.
method Applied Engle-Granger cointegration test and Log-Periodic-Power-Law-Singularity (LPPLS) model.
result Evidence of unsustainable speculative behaviors in Chinese real estate markets.
Credit expansion led to stronger household leverage cycles during the U.S. business cycle.
problem Understanding the role of credit supply in the U.S. business cycle.
method Causal evidence from 1999-2010 U.S. business cycle data.
result Credit expansion, particularly in private-label mortgages, caused stronger household leverage cycles.
We propose a novel multi-layered nonlinear model that is able to capture and predict the housing-demographic dynamics of the real-state market by simulating the transitions of owners among price-based house layers. This model allows us to determine which parameters are most effective to smoothen the severity of a poten…
Speculative bubbles have been occurring periodically in local or global real estate markets and are considered a potential cause of economic crises. In this context, the detection of explosive behaviors in the financial market and the implementation of early warning diagnosis tests are of critical importance. The recen…
Deep Huber QRNs predict Huber quantiles for house prices.
problem Predicting more functionals of predictive probability distributions.
method Training a DL algorithm with the Huber quantile scoring function.
result DHQRNs provide satisfactory absolute performance in house price prediction.
The Generalized Beta Prime distribution explains wealth and income distributions.
problem Explaining wealth and income distributions using a stochastic model.
method Using housing sale prices as a proxy, we numerically and analytically explore the properties of the Generalized Beta Prime distribution and its inequality indices.
result The Generalized Beta Prime distribution is a successful model for wealth and income distributions, with Hoover and Theil L being more appropriate for distributions with fat tails.
Model predicts climate change's impact on real estate prices.
problem Impact of climate transition on real estate prices.
method Modeling property valuation using Ornstein-Uhlenbeck processes and carbon prices.
result Depreciation of inefficient real estate assets due to climate transition is quantifiable.
Examines how extending home loan durations affects French households financially.
problem Financial implications for households with extended home loan durations.
method Analysis of French and international home loan systems, including bullet loans and Japanese home loans.
result Extending home loan durations can reduce monthly payments but raises financial risks.
Unified theory explains housing cycle across metros, showing credit expansion impacts.
problem Puzzling correlations between income and mortgage growth across ZIP codes and metros.
method Unified credit expansion theory, double differences, instrumental variables.
result Credit expansion drives housing cycle, affecting boom, bust, and recovery phases.
This paper uses alternative data to forecast Japanese real estate performance.
problem Accurate rent and price forecasting in Japanese real estate markets.
method Created a comprehensive house price index using over 5 million transactions and economic factors.
result Alternative data variables can forecast real estate performance effectively.
Study evaluates and compares traditional and causal machine learning methods for estimating direct price effects of environmental amenities.
problem Estimating direct price effects of environmental amenities in housing markets.
method Empirical Monte Carlo simulation to compare traditional regression and causal machine learning approaches.
result Causal Machine Learning (CML) methods, particularly causal forest DID, perform comparably to generalized DID in most scenarios.
The paper refutes standard asset pricing models and introduces new theories.
problem Inaccuracies in standard asset pricing models.
method Introduces new theories and empirical tests to explain asset pricing anomalies.
result New theories explain why standard models are inaccurate and provide insights.
The paper discusses various practical consequences of treating economics and finance as an inherently dynamic and chaotic system. On the theoretical side this looks at the general applicability of the market-making pricing approach to economics in general. The paper also discuses the consequences of the endogenous crea…
HabitatAgent offers a multi-agent system for transparent housing consultation.
problem Opaque reasoning and brittle multi-constraint handling in housing recommendation systems.
method HabitatAgent is a multi-agent architecture with specialized roles for memory, retrieval, generation, and validation.
result HabitatAgent achieves 95% accuracy in real user consultation scenarios, significantly outperforming a strong baseline.
Elevating houses to flood risk increases uncertainty, leading to higher optimal elevations.
problem Deciding how high to elevate houses to manage riverine flood risks is complex due to uncertainties.
method Used a multi-objective robust decision-making framework to analyze uncertainties.
result Optimal house elevation can be significantly higher than FEMA's recommendation due to deep uncertainties.
A new online algorithm reduces Gaussian process complexity for real-time data.
problem High computational complexity in Gaussian process regression for sequential data.
method Ensemble Kalman Filter applied to Gaussian process regression.
result Reduces computational complexity while maintaining prediction accuracy.
Double machine learning improves causal effect estimation by relaxing assumptions.
problem Estimating causal effects with observational data.
method Double/debiased machine learning (DML) framework.
result DML improves adjustment for nonlinear confounding relationships.