Housing price prediction incorporating spatio-temporal dependency into machine learning algorithms

AI-Based on Machine Learning Methods for Urban Real Estate Prediction: A Systematic Survey

A house price prediction model will be an effective application to predict and evaluate the cost of a certain property using the attributes. The main focus here is determining the prices of houses with higher accuracy by taking the important factors that may affect the price. It now plays an important role as the first step in many websites that are available online that are used to predict the price of the houses. Most of the websites lack accuracy as they show different prices for similar kinds of houses. By using some of the machine learning and deep learning algorithms, a prediction model can be developed that gives the price of the property with higher accuracy. The models that are used in this work are selected by utilizing many machine learning and deep learning models. The final model is the one that gives the highest accuracy among all the models that are taken into account. The result gives us minimum error and maximum accuracy. 

A Hybrid Machine Learning Model for House Price Prediction

The nexus between transportation infrastructure and housing prices in metropolitan regions

Accurate land valuation is necessary for tax purposes, land resources allocation, real estate management and urban development and planning. Since various factors from different domains affect land prices through non-linear relationships, automating the land valuation process on a large scale is a complex task. Advanced technologies in big data analysis and artificial intelligence have demonstrated superior capabilities in knowledge extraction in such cases. Accordingly, this paper develops and compares the performance of four Automated Valuation Models (AVMs) based on machine learning and deep learning techniques utilizing physical, geographical, socio-economic, environmental, legal and planning factors in Melbourne Metropolitan, Australia. According to the results, the eXtreme Gradient Boosting (XGBoost) method outperforms other algorithms of Support Vector Regression (SVR), random forest and Deep Neural Network (DNN). This method has achieved the coefficient of determination (R2) of 0.862, Mean Absolute Percentage Error (MAPE) of 0.139, and normalized Root Mean Square Error (nRMSE) of 0.281. The achieved high accuracy is due to incorporating a wide range of driving factors and applying innovative feature selection and hyperparameter tuning procedures evaluating various possible feature sets and hyperparameters. Accordingly, this paper can contribute to research, governmental and industry-based activities in terms of developing AVMs for mass land valuation. 

Automated land valuation models: A comparative study of four machine learning and deep learning methods based on a comprehensive range of influential factors

The housing market is increasing huge, predicting housing prices is not only important for a business issue, but also for people. However, housing price fluctuations have a lot of influencing factors. Also, there is a non-linear relationship between housing prices and housing factors. Most econometric or statistical models cannot capture non-linear relationships yet. Therefore, we propose housing price prediction models based on deep learning methods, which can capture non-linear relationships. In this work, we construct a dataset, including the housing attributes data and macroeconomic data in Taiwan from January 2013 to December 2018. The housing attributes data includes two types of housing transactions, which are “land + building” (Type1) and “land + building + park” (Type2). Macroeconomic data includes housing investment demand ratio, owner-occupier housing ratio, housing price to income ratio, housing loan burden ratio, and housing bargaining space ratio. Then, this dataset is utilized to evaluate the prediction methods based on deep learning algorithms BPNN and CNN to predict housing prices. Experimental results show that CNN with housing features has the best prediction effect. This study can be used to develop targetted interventions aimed at the housing market.

Housing prices prediction with deep learning: an application for the real estate market in Taiwan

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Housing prices prediction with deep learning: an application for the real estate market in Taiwan