Machine Learning Approaches for Automated Property Valuation: A Systematic Review

Property valuation is a fundamental activity in real estate markets, serving critical functions in transactions, taxation, mortgage lending, and investment decision-making (Pagourtzi et al., 2003). Traditional valuation approaches rely heavily on the expertise of professional appraisers who assess property characteristics and market conditions to estimate value. However, the subjective nature of this process can lead to inconsistencies and potential biases (Crosby et al., 2018).

The emergence of big data and advances in machine learning have created opportunities to develop more objective and scalable valuation methods. Automated Valuation Models (AVMs) leverage statistical and machine learning techniques to estimate property values based on transaction data and property characteristics (Glumac & Des Rosiers, 2021). These models have gained significant traction in the industry, with major financial institutions and technology companies investing heavily in AVM development.

Despite the growing body of literature on machine learning-based property valuation, there remains a need for a comprehensive synthesis of existing research. Previous reviews have focused on specific aspects, such as particular algorithms (Abidoye & Chan, 2017) or geographic regions (Mayer et al., 2019). This paper aims to provide a holistic overview of the field, examining methodological advances, performance benchmarks, and practical implementation considerations.

The application of quantitative methods to property valuation has a long history, with the hedonic pricing model (Rosen, 1974) serving as the theoretical foundation for most approaches. Hedonic models decompose property prices into constituent attributes, allowing researchers to estimate implicit prices for individual characteristics such as location, size, and amenities.

2.1 Traditional Statistical Approaches

Multiple regression analysis has been the dominant technique in hedonic pricing studies. Ordinary Least Squares (OLS) regression provides interpretable coefficients but assumes linear relationships and homoscedasticity, assumptions often violated in real estate data (Fik et al., 2003). Researchers have addressed these limitations through various extensions, including log-linear specifications, spatial econometric models, and quantile regression.

Spatial econometric models explicitly account for spatial dependencies in property values. The Spatial Lag Model (SLM) incorporates the influence of neighboring property prices, while the Spatial Error Model (SEM) addresses spatially correlated error terms (Anselin, 1988). These models have demonstrated improved predictive performance in many empirical applications, particularly in dense urban markets where spatial spillovers are pronounced.

2.2 Machine Learning Methods

Machine learning methods offer several advantages over traditional regression approaches. They can automatically capture non-linear relationships and complex interactions without requiring explicit specification. Furthermore, ensemble methods combine multiple models to reduce overfitting and improve generalization performance (Breiman, 2001).

Random Forest (RF) has emerged as a particularly popular method in property valuation research. RF constructs multiple decision trees using bootstrap samples and random feature subsets, then aggregates predictions through averaging. Studies consistently report that RF outperforms linear regression models, with typical improvements in prediction accuracy of 20-40% as measured by mean absolute error (Antipov & Pokryshevskaya, 2012; Hong et al., 2020).

We conducted a systematic literature review following the PRISMA guidelines (Moher et al., 2009). The search strategy encompassed major academic databases including Web of Science, Scopus, and Google Scholar. We used the following search terms: ("property valuation" OR "real estate appraisal" OR "house price prediction") AND ("machine learning" OR "artificial intelligence" OR "neural network" OR "random forest" OR "deep learning").

The initial search yielded 1,247 records. After removing duplicates and screening titles and abstracts, 156 papers were selected for full-text review. We applied inclusion criteria requiring: (1) empirical application to residential or commercial property valuation, (2) use of at least one machine learning method, and (3) quantitative performance evaluation. The final sample comprised 87 studies.

Table 1 summarizes the performance of different machine learning methods across the reviewed studies. Ensemble methods demonstrated the strongest performance, with Random Forest achieving a median MdAPE of 6.2% and Gradient Boosting achieving 5.8%. Neural networks showed higher variance in performance, with MdAPE ranging from 4.5% to 12.3% depending on architecture and training approach.

A key finding is the importance of feature engineering in model performance. Studies that incorporated spatial features, such as distance to amenities and neighborhood characteristics, consistently achieved better results than those relying solely on property-level attributes. Geographic information systems (GIS) integration has become increasingly common, enabling the calculation of precise spatial metrics.

Recent studies have explored the integration of unstructured data sources. Computer vision techniques have been applied to property photographs to extract features related to property condition, architectural style, and view quality (Law et al., 2019). Natural language processing has been used to analyze property descriptions, capturing qualitative aspects not reflected in structured attributes (Sirmans et al., 2005).

This systematic review has examined the application of machine learning methods to automated property valuation. Our analysis of 87 studies reveals significant advances in prediction accuracy, with ensemble methods and deep learning approaches consistently outperforming traditional statistical models. However, challenges remain in terms of model interpretability, data quality, and transferability across markets.

For practitioners, we recommend starting with well-established methods such as Random Forest or Gradient Boosting before exploring more complex architectures. Careful attention to feature engineering, particularly spatial features, is essential for achieving optimal performance. For researchers, promising directions include the development of interpretable machine learning methods and the integration of multimodal data sources.