Real Estate

Modern approaches to building interpretable models of the property market using machine learning on the base of mass cadastral valuation ArXiv ID: 2506.15723 “View on arXiv” Authors: Irina G. Tanashkina, Alexey S. Tanashkin, Alexander S. Maksimchuik, Anna Yu. Poshivailo Abstract In this article, we review modern approaches to building interpretable models of property markets using machine learning on the base of mass valuation of property in the Primorye region, Russia. The researcher, lacking expertise in this topic, encounters numerous difficulties in the effort to build a good model. The main source of this is the huge difference between noisy real market data and ideal data which is very common in all types of tutorials on machine learning. This paper covers all stages of modeling: the collection of initial data, identification of outliers, the search and analysis of patterns in the data, the formation and final choice of price factors, the building of the model, and the evaluation of its efficiency. For each stage, we highlight potential issues and describe sound methods for overcoming emerging difficulties on actual examples. We show that the combination of classical linear regression with interpolation methods of geostatistics allows to build an effective model for land parcels. For flats, when many objects are attributed to one spatial point the application of geostatistical methods is difficult. Therefore we suggest linear regression with automatic generation and selection of additional rules on the base of decision trees, so called the RuleFit method. Thus we show, that despite such a strong restriction as the requirement of interpretability which is important in practical aspects, for example, legal matters, it is still possible to build effective models of real property markets. ...

Implementing Dynamic Pricing Across Multiple Pricing Groups in Real Estate ArXiv ID: 2411.07732 “View on arXiv” Authors: Unknown Abstract This article presents a mathematical model of dynamic pricing for real estate (RE) that incorporates multiple pricing groups, thereby expanding the capabilities of existing models. The developed model solves the problem of maximizing aggregate cumulative revenue at the end of the sales period while meeting the revenue and sales goals. A method is proposed for distributing aggregate cumulative revenue goals across different RE pricing groups. The model is further modified to account for the time value of money and the real estate value increase as construction progresses. The algorithm for constructing a pricing policy for multiple pricing groups is described, and numerical simulations are performed to demonstrate how the algorithm operates. ...

Dynamic Pricing for Real Estate ArXiv ID: 2408.12553 “View on arXiv” Authors: Unknown Abstract We study a mathematical model for the optimization of the price of real estate (RE). This model can be characterised by a limited amount of goods, fixed sales horizon and presence of intermediate sales and revenue goals. We develop it as an enhancement and upgrade of the model presented by Besbes and Maglaras now also taking into account variable demand, time value of money, and growth of the objective value of Real Estate with the development stage. ...

Transforming Japan Real Estate ArXiv ID: 2405.20715 “View on arXiv” Authors: Unknown Abstract The Japanese real estate market, valued over 35 trillion USD, offers significant investment opportunities. Accurate rent and price forecasting could provide a substantial competitive edge. This paper explores using alternative data variables to predict real estate performance in 1100 Japanese municipalities. A comprehensive house price index was created, covering all municipalities from 2005 to the present, using a dataset of over 5 million transactions. This core dataset was enriched with economic factors spanning decades, allowing for price trajectory predictions. The findings show that alternative data variables can indeed forecast real estate performance effectively. Investment signals based on these variables yielded notable returns with low volatility. For example, the net migration ratio delivered an annualized return of 4.6% with a Sharpe ratio of 1.5. Taxable income growth and new dwellings ratio also performed well, with annualized returns of 4.1% (Sharpe ratio of 1.3) and 3.3% (Sharpe ratio of 0.9), respectively. When combined with transformer models to predict risk-adjusted returns 4 years in advance, the model achieved an R-squared score of 0.28, explaining nearly 30% of the variation in future municipality prices. These results highlight the potential of alternative data variables in real estate investment. They underscore the need for further research to identify more predictive factors. Nonetheless, the evidence suggests that such data can provide valuable insights into real estate price drivers, enabling more informed investment decisions in the Japanese market. ...

Hedonic Models Incorporating ESG Factors for Time Series of Average Annual Home Prices ArXiv ID: 2404.07132 “View on arXiv” Authors: Unknown Abstract Using data from 2000 through 2022, we analyze the predictive capability of the annual numbers of new home constructions and four available environmental, social, and governance factors on the average annual price of homes sold in eight major U.S. cities. We contrast the predictive capability of a P-spline generalized additive model (GAM) against a strictly linear version of the commonly used generalized linear model (GLM). As the data for the annual price and predictor variables constitute non-stationary time series, to avoid spurious correlations in the analysis we transform each time series appropriately to produce stationary series for use in the GAM and GLM models. While arithmetic returns or first differences are adequate transformations for the predictor variables, for the average price response variable we utilize the series of innovations obtained from AR(q)-ARCH(1) fits. Based on the GAM results, we find that the influence of ESG factors varies markedly by city, reflecting geographic diversity. Notably, the presence of air conditioning emerges as a strong factor. Despite limitations on the length of available time series, this study represents a pivotal step toward integrating ESG considerations into predictive real estate models. ...

ARED: Argentina Real Estate Dataset ArXiv ID: 2403.00273 “View on arXiv” Authors: Unknown Abstract The Argentinian real estate market presents a unique case study characterized by its unstable and rapidly shifting macroeconomic circumstances over the past decades. Despite the existence of a few datasets for price prediction, there is a lack of mixed modality datasets specifically focused on Argentina. In this paper, the first edition of ARED is introduced. A comprehensive real estate price prediction dataset series, designed for the Argentinian market. This edition contains information solely for Jan-Feb 2024. It was found that despite the short time range captured by this zeroth edition (44 days), time dependent phenomena has been occurring mostly on a market level (market as a whole). Nevertheless future editions of this dataset, will most likely contain historical data. Each listing in ARED comprises descriptive features, and variable-length sets of images. ...

Thailand Asset Value Estimation Using Aerial or Satellite Imagery ArXiv ID: 2307.08650 “View on arXiv” Authors: Unknown Abstract Real estate is a critical sector in Thailand’s economy, which has led to a growing demand for a more accurate land price prediction approach. Traditional methods of land price prediction, such as the weighted quality score (WQS), are limited due to their reliance on subjective criteria and their lack of consideration for spatial variables. In this study, we utilize aerial or satellite imageries from Google Map API to enhance land price prediction models from the dataset provided by Kasikorn Business Technology Group (KBTG). We propose a similarity-based asset valuation model that uses a Siamese-inspired Neural Network with pretrained EfficientNet architecture to assess the similarity between pairs of lands. By ensembling deep learning and tree-based models, we achieve an area under the ROC curve (AUC) of approximately 0.81, outperforming the baseline model that used only tabular data. The appraisal prices of nearby lands with similarity scores higher than a predefined threshold were used for weighted averaging to predict the reasonable price of the land in question. At 20% mean absolute percentage error (MAPE), we improve the recall from 59.26% to 69.55%, indicating a more accurate and reliable approach to predicting land prices. Our model, which is empowered by a more comprehensive view of land use and environmental factors from aerial or satellite imageries, provides a more precise, data-driven, and adaptive approach for land valuation in Thailand. ...

Pockets of Poverty: The Long-Term Effects of Redlining ArXiv ID: ssrn-2852856 “View on arXiv” Authors: Unknown Abstract This paper studies the long-term effects of redlining policies that restricted access to credit in urban communities. For empirical identification, we use a reg Keywords: redlining, credit access, long-term effects, urban communities, empirical identification, Real Estate Complexity vs Empirical Score Math Complexity: 3.0/10 Empirical Rigor: 8.0/10 Quadrant: Street Traders Why: The paper’s econometrics involve standard regression discontinuity design (RDD) and estimation techniques, resulting in moderate math complexity. However, the study is data-intensive, relying on historical census data and geocoded HOLC maps, and presents robust empirical findings designed for policy implications, placing it in the Street Traders quadrant. flowchart TD A["Research Question: Long-Term Effects of Redlining on Urban Poverty"] --> B["Methodology: Quasi-Experimental Design"] B --> C["Data Input: 1930s HOLC Redlining Maps & Modern Census Data"] C --> D{"Spatial & Regression Analysis"} D --> E["Computation: Comparing Areas Inside vs. Outside Redlined Zones"] E --> F["Key Finding: Persistent Poverty & Lower Credit Access"] F --> G["Outcome: Causal Link between Historical Redlining & Modern Inequality"]

Explaining the Housing Bubble ArXiv ID: ssrn-1669401 “View on arXiv” Authors: Unknown Abstract There is little consensus as to the cause of the housing bubble that precipitated the financial crisis of 2008. Numerous explanations exist: misguided monetary Keywords: Housing bubble, Financial crisis, Systemic risk, Real Estate Complexity vs Empirical Score Math Complexity: 2.5/10 Empirical Rigor: 4.0/10 Quadrant: Philosophers Why: The paper is primarily an economic and legal analysis of the housing bubble, relying on theoretical frameworks like information asymmetry and supply-side explanations with minimal advanced mathematics. While it uses historical data and discusses market mechanisms, it lacks backtests, quantitative models, or implementation-heavy empirical validation. flowchart TD A["Research Question: Causes of the 2008 Housing Bubble"] --> B["Data Collection: Financial, Macroeconomic, & Real Estate Data"] B --> C["Methodology: Econometric Analysis & Risk Modeling"] C --> D{"Computational Process: Identification of Systemic Risk Drivers"} D --> E["Key Finding: Inadequate Capital Buffers & Misguided Monetary Policy"] D --> F["Key Finding: Complex Derivatives Amplified Market Volatility"] E --> G["Outcome: Framework for Macroprudential Regulation"] F --> G