An Overview of Real Estate Modelling Techniques for House Price Prediction

Mohd, Thuraiya; Jamil, Nur Syafiqah; Johari, Noraini; Abdullah, Lizawati; Masrom, Suraya

doi:10.1007/978-981-15-3859-9_28

Cited by 17 publications

(8 citation statements)

References 27 publications

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“…House price prediction models not only provide property value estimation but also enable systematic planning and policy development. Hedonic Pricing, Nominal Valuation, Multiple Regression Analysis (MRA), Ensemble Regression Methods, and Artificial Neural Networks (ANN) are some of the most used methods for mass property valuation (Pagourtzi et al 2003; Jahanshiri, Buyong, and Shariff 2011; Wang and Li 2019; Mohd et al 2020).…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Approach for Mass Valuation of Residential Properties through Geographic Information Systems and Machine Learning Integration

Mete

Yomralıoğlu

2022

Geographical Analysis

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Geographic Information Systems (GIS) and Machine Learning methods are now widely used in mass property valuation using the physical attributes of properties. However, locational criteria, such as as proximity to important places, sea or forest views, flat topography are just some of the spatial factors that affect property values and, to date, these have been insufficiently used as part of the valuation process. In this study, a hybrid approach is developed by integrating GIS and Machine Learning for mass valuation of residential properties. GIS‐based Nominal Valuation Method was applied to carry out proximity, terrain, and visibility analyses using Ordnance Survey and OpenStreetMap data, than land value map of Great Britain was produced. Spatial criteria scores obtained from the GIS analyses were included in the price prediction process in which global and spatially clustered local regression models are built for England and Wales using Price Paid Data and Energy Performance Certificates data. Results showed that adding locational factors to the property price data and applying a novel nominally weighted spatial clustering algorithm for creating a local regression increased the prediction accuracy by about 45%. It also demonstrated that Random Forest was the most accurate ensemble model.

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Section: Introductionmentioning

confidence: 99%

A Hybrid Approach for Mass Valuation of Residential Properties through Geographic Information Systems and Machine Learning Integration

Mete

Yomralıoğlu

2022

Geographical Analysis

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“…Among them, what attracts attention is the exploration of the factors that cause changes in real estate land prices [19]. Various factors, such as transportation access, financial stability, and stocks exist as factors which can change the land prices [26,27,54,55], but in the current study, based on macro and micro factors-which are frequently used terms in the economy-we analysed which ones have a greater influence. In addition, the technologies used for real estate Big Data analysis include artificial neural network analysis, data mining, and machine learning, but in the current study, through regression analysis and correlation analysis, we investigated which factors are influential, and explained the correlation well.…”

Section: Discussionmentioning

confidence: 99%

“…Based on these technologies, the technology for analyzing real estate prices of public and private institutions introduced in Section 2.4 was created [7]. In addition, technologies used in real estate-related analysis technologies are being studied through regression analysis [19][20][21][22], artificial neural networks [23], data mining [24], predictive modeling [25,26], and machine learning [27]. Research should be conducted based on accuracy and efficiency.…”

Section: Real Estate Market Big Data Analysis System and Techniquementioning

confidence: 99%

Land Price Forecasting Research by Macro and Micro Factors and Real Estate Market Utilization Plan Research by Landscape Factors: Big Data Analysis Approach

Lee

Kim

2021

Symmetry

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In real estate, there are various variables for the forecasting of future land prices, in addition to the macro and micro perspectives used in the current research. Examples of such variables are the economic growth rate, unemployment rate, regional development and important locations, and transportation. Therefore, in this paper, data on real estate and national price fluctuation rates were used to predict the ways in which future land prices will fluctuate, and macro and micro perspective variables were actively utilized in order to conduct land analysis based on Big Data analysis. We sought to understand what kinds of variables directly affect the fluctuation of the land, and to use this for future land price analysis. In addition to the two variables mentioned above, the factor of the landscape was also confirmed to be closely related to the real estate market. Therefore, in order to check the correlation between the landscape and the real estate market, we will examine the factors which change the land price in the landscape district, and then discuss how the landscape and real estate can interact. As a result, re-explaining the previous contents, the future land price is predicted by actively utilizing macro and micro variables in real estate land price prediction. Through this method, we want to increase the accuracy of the real estate market, which is difficult to predict, and we hope that it will be useful in the real estate market in the future.

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“…Considering only trips by EDA residents leads to rides to be priced in accordance with the riders' ability to afford them. In Section 4 we compare FairRide with machine learning models commonly employed for pricing in the literature [9,21,15,16,29,1], and find that looking only at EDAtrips leads to most models resulting in more trips for EDA residents than the current pricing mechanism, and lead to a higher relative rideability (R 2 ), and FairRide outperforms them all. A naive baseline of simply applying a $5 discount on all rides is also implemented.…”

Section: Variable Discounting: Fairridementioning

confidence: 99%

“…FairRide We compare FairRide to machine learning models used for pricing [9,21,15,16,29,1]. We also compare against a naive baseline, which is a discount of $5 applied to all EDA rides.…”

Section: Pricing Mechanismsmentioning

confidence: 99%

Unveiling and Mitigating Bias in Ride-Hailing Pricing for Equitable Policy Making

Saxena¹,

Zhang²,

Shahabi³

2023

Preprint

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Ride-hailing services have skyrocketed in popularity due to the convenience they offer, but recent research has shown that their pricing strategies can have a disparate impact on some riders, such as those living in disadvantaged neighborhoods with a greater share of residents of color or residents below the poverty line. Since these communities tend to be more dependent on ride-hailing services due to lack of adequate public transportation, it is imperative to address this inequity. To this end, this paper presents the first thorough study on fair pricing for ride-hailing services by devising applicable fairness measures and corresponding fair pricing mechanisms. By providing discounts that may be subsidized by the government, our approach results in an increased number and more affordable rides for the disadvantaged community. Experiments on realworld Chicago taxi data confirm our theoretical findings which provide a basis for the government to establish fair ride-hailing policies.

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An Overview of Real Estate Modelling Techniques for House Price Prediction

Cited by 17 publications

References 27 publications

A Hybrid Approach for Mass Valuation of Residential Properties through Geographic Information Systems and Machine Learning Integration

A Hybrid Approach for Mass Valuation of Residential Properties through Geographic Information Systems and Machine Learning Integration

Land Price Forecasting Research by Macro and Micro Factors and Real Estate Market Utilization Plan Research by Landscape Factors: Big Data Analysis Approach

Unveiling and Mitigating Bias in Ride-Hailing Pricing for Equitable Policy Making

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