A model for predicting price polarity of real estate properties using information of real estate market websites

This paper presents a model that uses the information that sellers publish in real estate market websites to predict whether a property has higher or lower price than the average price of its similar properties. The model learns the correlation between price and information (text descriptions and features) of real estate properties through automatic identification of latent semantic content given by a machine learning model based on doc2vec and xgboost. The proposed model was evaluated with a data set of 57,516 publications of real estate properties collected from 2016 to 2018 of Bogot\'a city. Results show that the accuracy of a classifier that involves text descriptions is slightly higher than a classifier that only uses features of the real estate properties, as text descriptions tends to contain detailed information about the property

Visual Estimation of Building Condition with Patch-level ConvNets

The condition of a building is an important factor for real estate valuation. Currently, the estimation of condition is determined by real estate appraisers which makes it subjective to a certain degree. We propose a novel vision-based approach for the assessment of the building condition from exterior views of the building. To this end, we develop a multi-scale patch-based pattern extraction approach and combine it with convolutional neural networks to estimate building condition from visual clues. Our evaluation shows that visually estimated building condition can serve as a proxy for condition estimates by appraisers.Comment: To appear in: Workshop on Multimedia for Real Estate Tech, ICMR 2018, Yokohama, Japa

Who performs better? AVMs vs hedonic models

Purpose: In the literature there are numerous tests that compare the accuracy of automated valuation models (AVMs). These models first train themselves with price data and property characteristics, then they are tested by measuring their ability to predict prices. Most of them compare the effectiveness of traditional econometric models against the use of machine learning algorithms. Although the latter seem to offer better performance, there is not yet a complete survey of the literature to confirm the hypothesis. Design/methodology/approach: All tests comparing regression analysis and AVMs machine learning on the same data set have been identified. The scores obtained in terms of accuracy were then compared with each other. Findings: Machine learning models are more accurate than traditional regression analysis in their ability to predict value. Nevertheless, many authors point out as their limit their black box nature and their poor inferential abilities. Practical implications: AVMs machine learning offers a huge advantage for all real estate operators who know and can use them. Their use in public policy or litigation can be critical. Originality/value: According to the author, this is the first systematic review that collects all the articles produced on the subject done comparing the results obtained

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