Predicting property prices with machine learning algorithms

Ho, Winky K.O.; Tang, Bo; Wong, Siu Wai

doi:10.1080/09599916.2020.1832558

Cited by 157 publications

(111 citation statements)

References 56 publications

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“…Table 1 presented the dataset description with the mean, median and standard deviation computed for ten feature variables surveyed, including eight internal structure characteristics and two external characteristics [ 59 , 62 ]. Resulting from the comparison of the median and the mean for the variables in Table 1 , the samples collected might be considered to obey normal distribution, essentially providing unbiased baselines to characterize and understand the effect of the features of interest on housing prices in Boston.…”

Section: Resultsmentioning

confidence: 99%

Lie symmetry analysis of the effects of urban infrastructures on residential property values

et al. 2021

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Due to the complexity of socio-economic-related issues, people thought of housing market as a chaotic nucleus situated at the intersection of neighboring sciences. It has been known that the dependence of house features on the residential property value can be estimated employing the well-established hedonic regression analysis method in teams of location characteristic, neighborhood characteristic and structure characteristic. However, to further assess the roles of urban infrastructures in housing markets, we proposed a new kind of volatility measure for house prices utilizing the Lie symmetry analysis of quantum theory based on Schrödinger equation, mainly focusing on the effects of transportation systems and public parks on residential property values. Based on the municipal open government data regularly collected for four cities, including Boston, Milwaukee, Taipei and Tokyo, and all spatial sampling sites were featured by United States Geological Survey (USGS) National Map, transportation and park were modelled as perturbations to the quantum states generated by the feature space in response to the environmental amenities with different spatial extents. In an attempt to ascertain the intrinsic impact of the location-dependent price information obtained, the similarity functions associated with the Schrödinger equation were considered to facilitate revealing the city amenities capitalizing into house prices. By examining the spatial spillover phenomena of house prices in the four cities investigated, it was found that the mass transit systems and the public green lands possessed the infinitesimal generators of Lie point symmetries Y2 and Y5, respectively. Compared statistically with the common performance criteria, including mean absolute error (MAE), mean squared error (MSE) and, root mean squared error (RMSE) obtained by hedonic pricing model, the Lie symmetry analysis of the Schrödinger equation approach developed herein was successfully carried out. The invariant-theoretical characterizations of economics-related phenomena are consonant with the observed residential property values of the cities internationally, ultimately leading to develop a new perspective in the global financial architecture.

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Section: Resultsmentioning

confidence: 99%

Lie symmetry analysis of the effects of urban infrastructures on residential property values

et al. 2021

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“…Each feature was generated with the chosen algorithms which were evaluated using the performance metrics i.e., the R² and the RMSE. The Root Mean Square Error is a standard way for measuring how errorless the model is in predicting quantitative data; meanwhile, the R² indicates the significant effects on the dependent variable (Ho, 2020;Yilmazer, 2020).…”

Section: Resultsmentioning

confidence: 99%

Application of Machine Learning in Analysing Historical and Non-Historical Characteristics of Heritage Pre-War Shophouses

Ja’afar

Mohamad

2021

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Real estate is complex and its value is influenced by many characteristics. However, the current practice in Malaysia shows that historical characteristics have not been given primary consideration in determining the value of heritage properties. Thus, the accuracy of the values produced is questionable. This paper aims to determine whether the historical characteristics of the pre-war shophouses at North-East Penang Island, Malaysia contribute any significance to their value. Several Machine Learning algorithms have been developed for this purpose namely Random Forest, Decision Tree, Lasso Regression, Ridge Regression and Linear Regression. The result shows that the Random Forest Regressor with historical characteristics is the best fitting model with higher values of R-squared (R²) and lowest value of Root Mean Square Error (RMSE). This indicates that the historical characteristics of the heritage property under study contribute to its significant value. By considering the historical characteristics, the property’s value can be better predicted.

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“…GTB is a tree-based ensemble model that combines numerous weak classifiers to provide accurate classification [ 35 ]. It is a marked improvement on the classification performance of RF models and can avoid the problem of multi-collinearity [ 24 , 25 ]. Furthermore, XGB is an optimized type of GTB model and is more efficient than other conventional models; in particular, it has the ability to prevent overfitting through regularization [ 24 ].…”

Section: Discussionmentioning

confidence: 99%

“…Whenever SNP data were missing, we imputed the mode of the same disease for each SNP. While tuning the hyperparameters, the hyperparameters are optimized through 5-fold cross-validation only for the training set ( Supplementary Table 1 ) whereas the optimization of hyperparameters was not executed in testing set [ 25 ].…”

Section: Methodsmentioning

confidence: 99%

Machine learning approaches for the genomic prediction of rheumatoid arthritis and systemic lupus erythematosus

et al. 2021

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Background Rheumatoid arthritis (RA) and systemic lupus erythematous (SLE) are autoimmune rheumatic diseases that share a complex genetic background and common clinical features. This study’s purpose was to construct machine learning (ML) models for the genomic prediction of RA and SLE. Methods A total of 2,094 patients with RA and 2,190 patients with SLE were enrolled from the Taichung Veterans General Hospital cohort of the Taiwan Precision Medicine Initiative. Genome-wide single nucleotide polymorphism (SNP) data were obtained using Taiwan Biobank version 2 array. The ML methods used were logistic regression (LR), random forest (RF), support vector machine (SVM), gradient tree boosting (GTB), and extreme gradient boosting (XGB). SHapley Additive exPlanation (SHAP) values were calculated to clarify the contribution of each SNPs. Human leukocyte antigen (HLA) imputation was performed using the HLA Genotype Imputation with Attribute Bagging package. Results Compared with LR (area under the curve [AUC] = 0.8247), the RF approach (AUC = 0.9844), SVM (AUC = 0.9828), GTB (AUC = 0.9932), and XGB (AUC = 0.9919) exhibited significantly better prediction performance. The top 20 genes by feature importance and SHAP values included HLA class II alleles. We found that imputed HLA-DQA1*05:01, DQB1*0201 and DRB1*0301 were associated with SLE; HLA-DQA1*03:03, DQB1*0401, DRB1*0405 were more frequently observed in patients with RA. Conclusions We established ML methods for genomic prediction of RA and SLE. Genetic variations at HLA-DQA1, HLA-DQB1, and HLA-DRB1 were crucial for differentiating RA from SLE. Future studies are required to verify our results and explore their mechanistic explanation.

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Predicting property prices with machine learning algorithms

Cited by 157 publications

References 56 publications

Lie symmetry analysis of the effects of urban infrastructures on residential property values

Lie symmetry analysis of the effects of urban infrastructures on residential property values

Application of Machine Learning in Analysing Historical and Non-Historical Characteristics of Heritage Pre-War Shophouses

Machine learning approaches for the genomic prediction of rheumatoid arthritis and systemic lupus erythematosus

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