A Gradient Boosting Method for Effective Prediction of Housing Prices in Complex Real Estate Systems

ِAlmaslukh, Bandar Saleh Mouhammed

doi:10.1109/taai51410.2020.00047

Cited by 6 publications

(2 citation statements)

References 17 publications

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“…The rapid change in these parameters with a lack of transparency generates inaccurate information that may adversely affect the property valuation process. 11,12 Property price estimate or prediction is not a new problem; it has undergone several attempts in previous literature. For instance, hedonic regression models are often used to estimate property prices, examine inflation, and explore the relationship between prices and their impact parameters.…”

Section: Introductionmentioning

confidence: 99%

House price prediction using hedonic pricing model and machine learning techniques

Zaki

Nayyar

Dalal

et al. 2022

Concurrency and Computation

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Summary The problem with property valuation is that it is extremely complex. It is difficult to objectively model the pricing process or fairly estimate a property value. Many factors can contribute to this complexity such as spatial and time factors. Evaluators and researchers have been trying to model the process for centuries. Up until recently, when computer‐aided valuation systems provided better solutions in the data evaluation and real estate valuation. Nevertheless, they may suffer from low transparency, inaccuracy, and inefficiency. This work explores the ability of machine learning techniques (MLTs) in enhancing economic activities by increasing the accuracy of house price prediction. In this article, XGBoost algorithm has been integrated with outlier sum‐statistic (OS) approach. In the real estate industry, the price of property plays a crucial role in economic growth. The research attempts to predict the price of a house using MLTs. Here, the price of the property is predicted using Extreme Gradient (XG) boosting algorithm and hedonic regression pricing. Both XGBoost and hedonic pricing models use 13 variables as inputs to predict house prices. The contribution of this research lies in the practicality of using XGboost technique to predict house prices. Finally, the accuracy of the prediction algorithms is reported with XGBoosting showing the highest accuracy of 84.1% while the accuracy of the hedonic regression algorithm is 42%.

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Section: Introductionmentioning

confidence: 99%

House price prediction using hedonic pricing model and machine learning techniques

Zaki

Nayyar

Dalal

et al. 2022

Concurrency and Computation

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“…The difference between boosting and bagging is that the boosting produces models in a sequence, and the final model reaches the highest accuracy, while the models in the bagging are created in parallel. GB is an algorithm that has been established based on traditional boosting methods, but it has the ability to reduce the residual of the previous trained model and build a new model in the gradient direction of the residual reduction (Almaslukh, 2020;Ho et al, 2021;Zulkifley et al, 2020).…”

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confidence: 99%

A Framework to Integrate Bim With Artificial Intelligence and Machine Learning-Based Property Valuation Methods

Jafary

Shojaei²,

Rajabifard³

et al. 2022

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Property valuation is of extreme importance since variations in the real estate market enormously influence people’s life. The main goal of Automated Valuation Models (AVMs) is to calculate the market value of a large number of properties with an acceptable accuracy. The Hedonic Price Model (HPM) is the most widely used AVM for the valuation purposes. Despite its simplicity, ease of use and straightforwardness, HPM lacks the capability to address the non-linear relationships between different value-related factors. Hence, researchers have developed other state-of-the-art property valuation methods based on the advancements in computer science including Artificial Intelligence (AI), Machine Learning (ML), computer vision and deep learning. Design, development, and validation of such advanced AVMs require establishment of a database including data on the different influential factors. Two types of factors are used in the literature, including textual and visual features. Reliable data sources are required for the implementation of AVMs since the accuracy of the provided valuations is definitely linked to the reliability of the used real estate databases. Building Information Modelling (BIM) provides precise information on different components of properties. Although some scholars have tried to use BIM for property valuation, BIM benefits in different valuation procedures have not been fully investigated. Hence, this paper provides a framework that consider BIM capabilities to be integrated with different stages and processes in property valuation, especially in relation to advanced AVMs based on AI and ML.

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Predicting Residential Property Valuation in Major Towns and Cities on Mainland Fiji

Maharaj

Kumar

Sharma

2023

Lecture Notes in Computer Science

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A Gradient Boosting Method for Effective Prediction of Housing Prices in Complex Real Estate Systems

Cited by 6 publications

References 17 publications

House price prediction using hedonic pricing model and machine learning techniques

House price prediction using hedonic pricing model and machine learning techniques

A Framework to Integrate Bim With Artificial Intelligence and Machine Learning-Based Property Valuation Methods

Predicting Residential Property Valuation in Major Towns and Cities on Mainland Fiji

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