Search Results (72)

The prediction of real estate values is vital for taxation, transactions, mortgages, and urban policy development. Values can be predicted more accurately by statistical or advanced methods together when the size of the data is huge. In metropolitan cities like İstanbul, where size of the real estate data is vast and complex, mass appraisal methods supported by Machine Learning offer a scalable and consistent alternative. This study employs six algorithms: Artificial Neural Network, Extreme Gradient Boosting, K-Nearest Neighbors, Support Vector Regression, Random Forest, and Semi-Log Regression, to estimate the values of real estate on both the Asian and European continent parts of İstanbul. In total, 168,099 residential properties were utilized along with 30 of their features from both sides of the Bosphorus. The results show that RF yielded the best performance in Beşiktaş, while XGBoost performed best in Üsküdar. ANN also produced competitive results, although slightly less accurate than those of XGBoost and RF. In contrast, traditional SVR and SLR models underperformed, especially in terms of R² and RMSE values. With its large-scale dataset, focusing on one of the greatest metropolitan areas, Istanbul, and the usage of multiple ML algorithms, this study stands as a comprehensive and practical contribution to the field of automated real estate valuation. Full article

The accurate estimation of urban property values is a key challenge for appraisers, market participants, financial institutions, and urban planners. In recent years, machine learning (ML) techniques have emerged as promising tools for price forecasting due to their ability to model complex relationships among variables. However, their application raises two main critical issues: (i) the risk of overfitting, especially with small datasets or with noisy data; (ii) the interpretive issues associated with the “black box” nature of many models. Within this framework, this paper proposes a methodological approach that addresses both these issues, comparing the predictive performance of three ML algorithms—k-Nearest Neighbors (kNN), Random Forest (RF), and the Artificial Neural Network (ANN)—applied to the housing market in the city of Salerno, Italy. For each model, overfitting is preliminarily assessed to ensure predictive robustness. Subsequently, the results are interpreted using explainability techniques, such as SHapley Additive exPlanations (SHAPs) and Permutation Feature Importance (PFI). This analysis reveals that the Random Forest offers the best balance between predictive accuracy and transparency, with features such as area and proximity to the train station identified as the main drivers of property prices. kNN and the ANN are viable alternatives that are particularly robust in terms of generalization. The results demonstrate how the defined methodological framework successfully balances predictive effectiveness and interpretability, supporting the informed and transparent use of ML in real estate valuation. Full article

The usable floor area is one of the key parameters when appraising residential property. In Poland, valuers have to base their analysis on data from the Real Estate Price Register (RCN) in order to value a property. Unfortunately, these data often turn out to be incomplete, especially with regard to floor area, which makes the selection of reference properties difficult and can lead to erroneous valuation results. To address this problem, a study was conducted that used linear models, non-linear models and machine learning algorithms to calculate the floor area of buildings with complex multi-pitched roofs. The analysis was conducted using data sourced from the Database of Topographic Objects (BDOT10k). Three key factors were identified to provide a reliable estimate of usable floor area: the covered area, the height of the building and, optionally, the number of storeys. The results show that the linear model based on the design data achieved an accuracy of 88%, the non-linear model achieved 89% and the machine learning algorithms achieved 93%. For the existing building data from the city of Koszalin, the best model achieved an accuracy of 90%. The estimated values of the usable area of the building designs for the best model on the test set differed on average from the true ones by 8.7 m², while for the existing buildings, the difference was 9.9 m² on average (in both cases, the average relative error was about 7%). Full article

The real estate sector is increasingly recognizing facility management (FM) as a key driver of asset value. Among emerging technologies, Building Information Modeling (BIM) and Building Management Systems (BMSs) stand out for their potential to enhance FM efficiency by integrating design data with building operations across the entire lifecycle, from construction to maintenance, performance monitoring, and renovation. While their technical applications have been widely studied, the financial impact of these tools on FM remains underexplored. This paper addresses that gap by estimating the economic value generated by implementing BIM and BMS in real estate facility management. Based on thirteen semi-structured interviews with professionals from the Italian real estate sector, we identified and quantified cost-saving factors and challenges related to digital adoption. These cost efficiencies, when recurring and quantifiable, can improve net operating income (NOI), thereby supporting higher asset valuations under income-based real estate appraisal methods. The results show that integrating BIM and BMS in facility management may generate average annual cost savings of 5.81% relative to asset value, with coordination improvements alone accounting for up to 3.28% per year. Based on a 30-year simulation, these savings correspond to a positive Net Present Value (NPV), supporting the financial viability of digital FM adoption in real estate. This study offers empirical evidence to support investment decisions in digital FM technologies and contributes to bridging the gap between innovation and financial evaluation in the real estate sector. Full article

In the realm of real estate, each property owns a unique set of characteristics that distinguish it from others. While each property has its own distinctive features, the appraisal process prioritises only those qualities that meaningfully affect the value in the given market context. However, in the dynamically evolving market situation, expectations of real estate buyers can also transform. This study aims to explore how the surrounding environment and micro-location aspects affect the property value, which can deliver valuable outcomes for real estate market participants and researchers. For that purpose, the authors selected nine factors, called non-market attributes, that may affect the estimated value: air quality, noise emissions, green areas, rivers and water reservoirs, kindergartens and primary schools, universities, medical facilities, shopping centres and religious buildings. Moreover, apart from non-market attributes, the authors selected six market attributes usually used for the determination of residential real estate values according to the Polish regulations in this field. The detailed analysis of factors influencing the property value has been conducted based on the residential apartments in the district Zwięczyca in Rzeszów. Specifically, with the use of Pearson’s total correlation coefficients, authors explored market and non-market attributes and examined their relationships with unit transaction prices, attempting to answer the research question on whether non-market attributes can differentiate market values of residential apartments, when local real estate markets are considered. The results demonstrate that all selected market factors have a visible effect on analysed real estate prices and might be adopted for appraisal. Among nine non-market factors, only three of them have a pronounced effect on prices and might be used for the valuation of residential properties on the local market. The combined database of market and non-market factors reveals eight attributes (five market and three non-market) affecting prices of residential apartments. Full article

The field of real estate valuations is multivariate in nature. Each property has different intrinsic attributes that have a bearing on its final value: location, use, purpose, access, the services available to it, etc. The appraiser analyzes all these factors and the current status of other similar properties on the market (comparable assets or units of comparison) subjectively, with no applicable rules or metrics, to obtain the value of the property in question. To model this context of subjectivity, this paper proposes the use of a fuzzy system. The inputs to the fuzzy system designed are the variables considered by the appraiser, and the output is the adjustment coefficient to be applied to the price of each comparable asset to obtain the price of the property to be appraised. To design this model, data have been extracted from actual appraisals conducted by three professional appraisers in the urban center of Santa Cruz de Tenerife (Canary Islands, Spain). The fuzzy system is a decision-helping tool in the real estate sector: appraisers can use it to select the most suitable comparables and to automatically obtain the adjustment coefficients, freeing them from the arduous task of calculating them manually based on the multiple parameters to consider. Finally, an evaluation is presented that demonstrates its applicability. Full article

One of the key elements in real estate appraisal of residential buildings is the usable area. To determine the monetary value of real estate, appraisers in Poland often rely on transaction data registered in the Real Estate Price Register (REPR). However, the REPR may contain meaningful gaps, particularly on information concerning usable areas. This may lead to difficulties in finding suitable comparative properties, resulting in mispricing of the property. To address this problem, we used linear and nonlinear models to estimate the usable area of buildings with multi-pitched roofs. Utilizing widely available data from the Topographic Objects Database (BDOT10k) based on LiDAR technology, we have shown that three parameters (building’s covered area, building’s height, and optionally the number of storeys) are sufficient for a reliable estimate of the usable area of a building. The best linear model, using design data from architectural offices, achieved a fit of 95%, while the best model based on real data of existing buildings in the city of Koszalin, Poland achieved 92% fit. The best nonlinear model achieved slightly better results than the linear model in the case of design data (better fit by approximately 0.2%). In the case of existing buildings in Koszalin, the best fit was at 93%. The proposed method may help property appraisers determine a more accurate estimation of the usable area of comparative buildings in the absence of this information in the REPR. Full article

The possibility of carrying out the cultivation of numerous plant species in vertical farming highlights the need for policy makers to determine the cadastral value of the buildings in which these production activities are carried out. In this regard, estimates of buildings intended to host vertical farming are illustrated according to the procedure established by Italian cadastral legislation, which establishes that the fiscal value of buildings intended for vertical farming must be estimated through their market value. Appraisals is carried out using the direct capitalization method but follow two different approaches. One approach is based on the expertise of the appraiser, who acts by making assessments through subjective and arbitrary choices. The other approach is based on the use of best practices, as indicated by international evaluation standards that follow appropriate methodologies. Our comparison between the two approaches focuses on determining the capitalization rate, which determines the estimated value. The market value estimated using the procedures recognized by the valuation standards appears to be more valid methodologically and more reliable. This is demonstrated by applying yield capitalization to the same income cash flow in both formulations. Additionally, through the identification of the conversion cash flow, useful details on financial flow can be obtained and used to determine the value. The obtained results may be useful for public operators for the purposes of determining the value of assets for tax purposes. More generally, they are also useful from a methodological and application point of view in real estate valuation and support the development of tools for making efficient investment choices. Full article

This study examines whether and through which channels ESG ratings influence key metrics in the real estate industry. Focusing on Switzerland as a case study and concentrating on commercial real estate investors and their income properties, we utilize unique datasets and employ an OLS post-LASSO estimation procedure to identify and quantify the associations between ESG ratings and four key metrics: appraisal-based and transaction-based discount rates, rental incomes, and vacancy rates. Our results demonstrate that ESG ratings maintain a significant association with all four key metrics even after undergoing robustness checks. When dissecting the total ESG rating into its components, the environmental rating stands out as the most significant. While largely dependent on the specific metric being analyzed, the association of social and governance ratings tends to be less pronounced. Delving deeper into individual ESG rating levels, our findings suggest potential signaling effects, as properties with higher ESG ratings demonstrate heightened sensitivity to both types of discount rates and vacancy rates. Overall, our findings deepen the understanding of the association between ESG ratings and real estate markets, illuminating the intersection of sustainability and financial relevance. Full article

Property valuation services using artificial intelligence (AI) have been developed, with more than 20 services available in Japan. However, since their algorithms and training data are not publicly available, the extent of variations in the AI property valuations among these services is not clear. This study focuses on five services and uses a sample of 4295 valuations for 859 condominium units in six popular residential areas in Tokyo. (1) Multiple comparison tests of the AI property valuations among the services are conducted to confirm their statistical significance and to examine the extent of the variations. (2) The business models of each service are compared to examine the factors contributing to these variations. The results showed that the average variation in the AI property valuations was 10.6%, which was larger than the variations observed in traditional property valuations. It was also found that the valuation groups, categorized as high or low, varied based on the business models of the service providers. These results indicate that it is necessary to promote the healthy development of AI property valuation by establishing guidelines, such as requiring the AI property valuation services to ensure fair prices or disclosing their algorithms and data. Full article

The housing market in Dar es Salaam, Tanzania, is expanding and with it a need for increased market transparency to guide investors and other stakeholders. The objective of this paper is to evaluate machine learning (ML) methods to appraise real estate in formal and informal housing markets in this nascent market sector. Various advanced ML models are applied with the aim of improving property value estimates in a market with limited access to information. The dataset used included detailed property characteristics and transaction data from both market types. Regression, decision trees, neural networks, and ensemble methods were employed to refine property appraisals across these settings. The findings indicate significant differences between formal and informal market valuations, demonstrating ML’s effectiveness in handling limited data and complex market dynamics. These results emphasise the potential of ML techniques in emerging markets where traditional valuation methods often fail due to the scarcity of transaction data. Full article

In the absence of comparative real estate data in the market segment of the property to be estimated, the appraiser may resort to income capitalization to estimate the market value. Often, however, the choice of which rate to apply is affected by subjective and arbitrary assessments. The estimation result can therefore be inaccurate and rather unclear. However, the Remote Segments Approach (RSA), through appropriate adjustments on the original values, prices, and incomes detected in the remote segments, makes it possible to arrive at an appraisal result consistent with estimative logic and real estate valuation standards. The proposed application illustrates the estimation of the market value of a specialized fruit orchard of avocado, which is to be considered new in relation to other fruit species already present in the reference area. The adjustments required by the RSA are solved with the General Appraisal System (GAS), defining the difference matrix based on relevant characters common to all segments considered. The application is carried out by comparing the segment in which the orchard being estimated falls (subject) with other remote market segments in which prices and incomes constituted by other tree crops are collected. The market value of the subject is derived by making adjustments to the prices and incomes observed in the remote segments of comparison with a comparison function constructed through relevant characters common to the segments considered. The comparison function makes it possible to arrive at the determination of the capitalization rate to be used in estimating the value of the fruit orchard by income approach. While it is based on the comparison of segments, the approach followed allows for a value judgment consistent with the estimation comparison and capable of providing a solution less conditioned by the appraiser’s expertise in the presence of particularly pronounced limiting conditions. Full article

This study addresses the scarcity of empirical findings on the determinants of discount rates in the Discounted Cash Flow (DCF) method, filling a crucial gap in the existing literature and enhancing the understanding of the valuation process from the perspectives of key stakeholders. Leveraging a unique dataset comprising market transactions enriched with expert-based valuation information, the study conducts a comprehensive counterfactual analysis of the fundamental determinants influencing both appraisal-based and transaction-based discount rates. The results reveal that appraisers and investors attribute different levels of importance to object-specific, locational, and macroeconomic variables. A type-specific analysis further reveals that locational and macroeconomic variables exert a greater influence on discount rates in the residential real estate segment. In contrast, object-specific characteristics hold significantly higher importance in explaining discount rates in the commercial real estate segment. Full article

Real estate mass appraisal is increasingly gaining popularity as a critical issue, reflecting its growing importance and widespread adoption in economic spheres. And data-driven machine learning methods have made new contributions to enhancing the accuracy and intelligence level of mass appraisal. This study employs python web scraping technology to collect raw data on second-hand house transactions spanning from January 2015 to June 2023 in China. Through a series of data processing procedures, including feature indicator acquisition, the removal of irrelevant sample cases, feature indicator quantification, the handling of missing and outlier values, and normalization, a dataset suitable for direct use by mass appraisal models is constructed. A dynamic neural network model composed of three cascaded sub-models is designed, and the optimal parameter combination for model training is identified using grid searching. The appraisal results demonstrate the reliability of the dynamic neural network model proposed in this study, which is applicable to real estate mass appraisal. A comparison with the common methods indicates that the proposed model exhibits a superior performance in real estate mass appraisal. Full article

In the face of pressing global challenges such as climate change, socioeconomic inequalities, and rapid urbanization, ensuring sustainable development in the regions has become essential. The COVID-19 pandemic has highlighted how vulnerable cities are to unforeseen crises and underscored the urgent need for proactive urban planning strategies capable of navigating dynamic and unpredictable futures. In this context, the use of geographic information systems (GIS) offers researchers and decision makers a distinct advantage in the study of spatial data and enables the comprehensive study of spatial and temporal patterns in various disciplines, including real estate valuation. Central to the integration of modern technology into real estate valuation is the need to mitigate the inherent subjectivity of traditional valuation methods while increasing efficiency through the use of mass appraisal techniques. This study draws on extensive academic literature comprising 103 research articles published between 1993 and January 2024 to shed light on the multifaceted application of GISs in real estate valuation. In particular, three main areas are addressed: (1) hedonic models, (2) artificial intelligence (AI), and mathematical appraisal models. This synthesis emphasizes the interdependence of numerous societal challenges and highlights the need for interdisciplinary collaboration to address them effectively. In addition, this study provides a repertoire of methodologies that underscores the potential of advanced technologies, including artificial intelligence, GISs, and satellite imagery, to improve the subjectivity of traditional valuation approaches and thereby promote greater accuracy and productivity in real estate valuation. By integrating GISs into real estate valuation methodologies, stakeholders can navigate the complexity of urban landscapes with greater precision and promote equitable valuation practices that are conducive to sustainable urban development. Full article