DoRA: Domain-Based Self-Supervised Learning Framework for Low-Resource Real Estate Appraisal

The marketplace system connecting demands and supplies has been explored to develop unbiased decision-making in valuing properties. Real estate appraisal serves as one of the high-cost property valuation tasks for financial institutions since it requires domain experts to appraise the estimation based on the corresponding knowledge and the judgment of the market. Existing automated valuation models reducing the subjectivity of domain experts require a large number of transactions for effective evaluation, which is predominantly limited to not only the labeling efforts of transactions but also the generalizability of new developing and rural areas. To learn representations from unlabeled real estate sets, existing self-supervised learning (SSL) for tabular data neglects various important features, and fails to incorporate domain knowledge. In this paper, we propose DoRA, a Domain-based self-supervised learning framework for low-resource Real estate Appraisal. DoRA is pre-trained with an intra-sample geographic prediction as the pretext task based on the metadata of the real estate for equipping the real estate representations with prior domain knowledge. Furthermore, inter-sample contrastive learning is employed to generalize the representations to be robust for limited transactions of downstream tasks. Our benchmark results on three property types of real-world transactions show that DoRA significantly outperforms the SSL baselines for tabular data, the graph-based methods, and the supervised approaches in the few-shot scenarios by at least 7.6% for MAPE, 11.59% for MAE, and 3.34% for HR10%. We expect DoRA to be useful to other financial practitioners with similar marketplace applications who need general models for properties that are newly built and have limited records. The source code is available at https://github.com/wwweiwei/DoRA.Comment: Accepted by CIKM 202

Property Appraisal Platform

This document focuses on the internship in the company DeepNeuronic as part of the project ”Property Appraisal Platform”. This project’s main objective was to develop machine learning models capable of inferring real estate prices using machine learning models and a limited set of features capable of describing a property. In order to achieve the objective, the project was divided into two major phases. In the first phase the state of the art was studied and a dataset collection was put together with the aim of creating a comprehensive representation of the real estate market all across the globe. With this dataset collection available, a set of features was chosen according to their relevancy for the main problem. The second phase consisted of the major practical developments, such as the model creation and dataset improvements. With this in mind, the most relevant metrics were chosen and the models were evaluated in the chosen datasets, creating a set of baseline results to improve upon. Afterwards, multiple other experiments were done, tackling different areas of interest that could potentially improve upon the performance of the models. In total, four different models were evaluated and all the experiments improved upon the baseline results. As an highlight, in the last experiment we propose the transformation of the target label from the property price to the ”Coefficient of the price per square meter compared to the suburb average”. Using this new target label, the results obtained were considerably better. All of these experiments were redone in a new more complex dataset, with all of the experiments improving upon the baseline results obtained in this dataset, reinforcing the idea that these experiments can be used even in more complex datasets.Este documento foi criado no âmbito do estágio realizado na empresa DeepNeuronic como parte do projeto ”Plataforma de Avaliação de Propriedades”. O objetivo do mesmo foi desenvolver modelos de aprendizagem automática capazes de avaliar preços do mercado imobiliário usando modelos inteligentes e um conjunto limitado de características capazes de descrever uma propriedade. Para atingir este objetivo o projeto foi dividido em duas partes principais. Na primeira parte foi feito um estudo intensivo do estado da arte, e criada uma coleção de bancos de dados extensiva, representante do mercado imobiliário no mundo inteiro. Com esta coleção disponível, um conjunto de características foram escolhidas de acordo com a sua relevância para o problema em questão. A segunda fase consistiu nos desenvolvimentos práticos principais, envolvendo a criação de modelos e melhorias nos bancos de dados. Para isso foram escolhidas as métricas mais relevantes, e foram avaliados os modelos nos bancos de dados iniciais, criando assim um conjunto de resultados base. Seguidamente, múltiplas experiências foram feitas, abordando diferentes áreas de interesse que podiam potencialmente melhorar os resultados base. No total quatro modelos diferentes foram avaliados e as experiências realizadas todas melhoraram os resultados base obtidos. De especial relevância, na última experiência propomos a transformação do preço da propriedade para uma variável objetivo que pode ser descrita como o ”Coeficiente do preço por metro de área quadrado comparado à média do subúrbio”. Usando esta variável os resultados obtidos foram consideravelmente melhores, estas experiências foram refeitas em um novo banco de dados consideravelmente mais complexo, verificando-se também que todas estas experiências melhoram os resultados obtidos inicialmente, reforçando a ideia que estas experiências podem ser usadas mesmo em bancos de dados mais complexos

Improving Real Estate Appraisal with POI Integration and Areal Embedding

Despite advancements in real estate appraisal methods, this study primarily focuses on two pivotal challenges. Firstly, we explore the often-underestimated impact of Points of Interest (POI) on property values, emphasizing the necessity for a comprehensive, data-driven approach to feature selection. Secondly, we integrate road-network-based Areal Embedding to enhance spatial understanding for real estate appraisal. We first propose a revised method for POI feature extraction, and discuss the impact of each POI for house price appraisal. Then we present the Areal embedding-enabled Masked Multihead Attention-based Spatial Interpolation for House Price Prediction (AMMASI) model, an improvement upon the existing ASI model, which leverages masked multi-head attention on geographic neighbor houses and similar-featured houses. Our model outperforms current baselines and also offers promising avenues for future optimization in real estate appraisal methodologies

Machine Learning Applications to Land and Structure Valuation

Acknowledgments: We thank Nicola Stalder and his IAZI team for preparing the dataset for the Swiss case study. The authors are grateful to the referees, whose feedback and comments have improved the quality of the paper.Peer reviewedPublisher PD

A Framework for Predicting the Optimal Price and Time to Sell a Home

Due to high barriers to conduct housing market research, many home sellers opt to go to the market with asymmetric information or invest large sums of money into hiring a professional. This research aims to reduce these inefficiencies by proposing a framework that provides sellers with a concrete recommendation on optimal time and price to sell a home to maximize financial gains. The core data used in this research is the NOVA Home Price dataset, which contains 34,973 house listings over multiple years in Northern Virginia. A pipeline of machine learning models, including a linear regression, random forest, XGboost and artificial neural network are trained and evaluated for performance on predicting home close prices. The final model employed is an ensemble of random forest and XGboost and is tested on both a holdout set of Northern Virginia data as well as real estate data scraped from Zillow to introduce some variance. To control for future economic trends, a long-short-term memory model is then trained using temporal data from the Federal Reserve. Finally, the algorithm distills the insights from the disparate models to provide recommendations on optimal time and price to go to market, as well as short-term investments to increase potential gains from sale. The study finds that home features coupled with macro-economic trends can offer home sellers strong recommendations on optimal time and price to list homes. This research is preliminary and should be used as a baseline for future studies

Suur-Helsingin kerrostalojen vuokrahintojen arvionti

Determining the optimal rental price of an apartment is typically something that requires a real estate agent to gauge the external and internal features of the apartment, and similar apartments in the vicinity of the one being examined. Hedonic pricing models that rely on regression are commonplace, but those that employ state of the art machine learning methods are still not widespread. The purpose of this thesis is to investigate an optimal machine learning method for predicting property rent prices for apartments in the Greater Helsinki area. The project was carried out at the behest of a client in the real estate investing business. We review what external and inherent apartment features are the most suitable for making predictions, and engineer additional features that result in predictions with the least error within the Greater Helsinki area. Combining public demographic data from Tilastokeskus (Statistics Finland) and data from the online broker Oikotie Oy gives rise to a model that is comparable to contemporary commercial solutions offered in Finland. Using inverse distance weighting to interpolate and generate a price for the coordinates of the new apartment was also found to be crucial in developing an performant model. After reviewing models, the gradient boosting algorithm XGBoost was noted to fare the best for this regression task

Deep Learning in Predicting Real Estate Property Prices: A Comparative Study

The dominant methods for real estate property price prediction or valuation are multi-regression based. Regression-based methods are, however, imperfect because they suffer from issues such as multicollinearity and heteroscedasticity. Recent years have witnessed the use of machine learning methods but the results are mixed. This paper introduces the application of a new approach using deep learning models to real estate property price prediction. The paper uses a deep learning approach for modeling to improve the accuracy of real estate property price prediction with data representing sales transactions in a large metropolitan area. Three deep learning models, LSTM, GRU and Transformer, are created and compared with other machine learning and traditional models. The results obtained for the data set with all features clearly show that the RF and Transformer models outperformed the other models. LSTM and GRU models produced the worst results, suggesting that they are perhaps not suitable to predict the real estate price. Furthermore, the implementations of Transformer and RF on a data set with feature reduction produced even more accurate prediction results. In conclusion, our research shows that the performance of the Transformer model is close to the RF model. Both models produce significantly better prediction results than existing approaches in terms of accuracy

House Price Prediction using Machine Learning Algorithms

House prices are a major financial decision for everyone involved in the housing market, including potential home buyers. A major part of the real estate industry is housing. An accurate housing price prediction is a valuable tool for buyer and seller as well as real estate agents. The study is done for the purpose of knowledge among the people to understand and estimate the pricing of their houses. The prediction will be made using four machine learning algorithms such as linear regression, polynomial regression, random forest, decision tree. Linear Regression has good interpretability. Decision tree is a graphical representation of all possible solutions. Polynomial regression can be easily fitted to a wide variety of curves. Regression and classification issues are resolved with random forests .Among the given algorithm, Random forest provides better accuracy of about 89% for given dataset