Estimation of Housing Prices by Fuzzy Regression and Artificial Neural Network

Ghodsi, Reza; Boostani, Abtin; Faghihi, Farshid

doi:10.1109/ams.2010.29

Cited by 10 publications

(6 citation statements)

References 29 publications

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“…The use of ANN in explaining the importance of each attribute when determining house prices has gained momentum in recent literature (Abarca, 2021;Ghodsi et al, 2010;Ghorbani & Afgheh, 2017;Nemati et al, 2020). According to Limsombunchai (2004), a neural network is an artificial intelligence model created to mimic the human brain's learning process.…”

Section: Methodsmentioning

confidence: 99%

Willingness to accept and willingness to pay for residential properties: a hedonic model approach

Mishi,

Mwanyepedza

2023

EOR

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The world over is becoming urbanized, and people are migrating to cities in large numbers in search of opportunities. The increased urbanization has posed challenges such as congestion, rising crime, and growing urban poverty. The governments respond by providing amenities such as schools, hospitals, and housing to meet to increase in demand for these facilities. However, there is a need for the provision of facilities that meets the expectations of the people, particularly on the proximity of amenities and bundles of utility-bearing housing characteristics. In an attempt to address the challenge mentioned, the study estimated the hedonic characteristics influencing the willingness to accept and willingness to pay for housing facilities in the Eastern Cape Province, South Africa. Using a multiple linear regression model and artificial neural network, the study found out that properties with a bathroom, garage and large floor size have a higher value compared to properties without these facilities.When making decisions to acquire a property, buyers consider the availability of discounts and the prevailing property price. Overall, willingness to pay and accept decisions are mainly determined by location and the price at which homogeneous neighborhood properties were sold. Therefore, the study recommends that urban town planners and other housing authorities prioritize the construction of properties with larger floor areas, parking bays, and bathrooms using a cost-effective mechanism that makes the properties affordable to residents.

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Section: Methodsmentioning

confidence: 99%

Willingness to accept and willingness to pay for residential properties: a hedonic model approach

Mishi,

Mwanyepedza

2023

EOR

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“…They found that the support vector regression method performed better in estimating the house price. Ghodsi et al (2010), in their study on the estimation of Iranian housing prices, tested economic variables including income from oil, housing price index, general price index, cost of construction materials and gross domestic product (GDP) using artificial neural networks architecture. According to the test results, back propagation artificial neural network technique (MAPE-Mean Absolute Percent Error) 0.11698 has been formed.…”

Section: Literature Researchmentioning

confidence: 99%

“…However, these methods are quite successful in terms of the results of least-squares vector regression compared to other methods. Ghodsi et al (2010) used economic variables including income from oil, general price index, housing price index, gross domestic product (GDP) and cost of construction materials as independent variables in their study on the estimation of Iranian housing prices. They used artificial neural network architecture as a method.…”

Section: Literature Researchmentioning

confidence: 99%

Forecasting housing prices in Turkey by machine learning methods

2022

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In this study, decision tree regression, artificial neural networks (ANN) and support vector machines (SVM) methods are applied by using monthly data for the period 2013-2020 in the estimation of housing sales in Turkey. In the analysis, the volume of individual mortgage loans offered by banks, the average annual interest rate of mortgage loans from macroeconomic and market variables, the consumer price index (CPI), the BIST 100 index, the benchmark bond interest rate, gold prices and the values of the US dollar and Euro Turkish lira and the housing sales price per square meter in Turkey are used. As a result of the analysis carried out on the model created house sales prices in the Turkish housing market have been successfully estimated and in the light of these estimates, it is determined that banks can guide banks in the creation of various credit packages and appropriate loan targets to support the housing sector.

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“…The percentages of missing values of variable Alley and Fence are 93.22% and 80.44%. All four of these variables have an extremely high amount of missing values, leading to inaccurate results if we keep them in the dataset [2]. Thus, the author decided to remove these four variables from the dataset.…”

Section: Data Preparationmentioning

confidence: 99%

Prediction on Housing Price Based on the Data on Kaggle

Yu¹

2022

Proceedings of the 2022 3rd International Conference on E-Commerce and Internet Technology (ECIT 2022)

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People's lives have always relied on having a secure place to stay. As a result, housing prices have risen to people's top priority list. This paper uses a series of correlation tests, exploratory data analysis, and feature selection approaches to the training and testing datasets to find the most accurate model for forecasting housing prices. There is a dataset about housing prices on Kaggle. The author found that the variables PoolQC, MiscFeature, Alley, and Fence include almost 90% missing values during the data preparation process. After removing those variables, the author generated a correlation matrix to visualize the relationship between the rest variables. In addition, the exploratory data analysis on the dataset shows the overall quality of a home, the size of the living area, the total basement size, and the presence of newer homes contribute the most to a house's value. The author created seven different machine learning models and calculated the R-square and root mean square values. Among these models, the random forest algorithm has the highest R-Squared value and the lowest RMSE. As a result, the random forest algorithm is the best model for predicting the price of a house.

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Estimation of Housing Prices by Fuzzy Regression and Artificial Neural Network

Cited by 10 publications

References 29 publications

Willingness to accept and willingness to pay for residential properties: a hedonic model approach

Willingness to accept and willingness to pay for residential properties: a hedonic model approach

Forecasting housing prices in Turkey by machine learning methods

Prediction on Housing Price Based on the Data on Kaggle

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