Real Estate Appraisals with Bayesian Approach and Markov Chain Hybrid Monte Carlo Method: An Application to a Central Urban Area of Naples

Giudice, Vincenzo Del; Paola, Pierfrancesco De; Forte, Fabiana; Manganelli, Benedetto

doi:10.3390/su9112138

Cited by 35 publications

(18 citation statements)

References 59 publications

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“…Sirmans et al [45] pointed out that taking the logarithm of housing prices in the Hedonic price model was a common assumption for normalizing data distribution and reducing error terms. The analysis by Del Giudice et al [46] estimates a hedonic price function using a semiparametric regression based on Penalized Spline Smoothing, and compares the price prediction performance with conventional parametric models. The excellent results obtained show that the semiparametric models allow researchers to obtain significant improvement in the prediction of housing sale prices.…”

Section: Empirical Modelmentioning

confidence: 99%

House Prices, Mortgage Rate, and Policy: Megadata Analysis in Taipei

Chen

2018

Sustainability

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There are intensive concerns about the causes of rising housing prices in Taipei. The aims of this study are twofold. The first addresses the issue of whether the low-interest-rate policy is adversely driving housing prices in the metropolitan area of Taipei. The second is to investigate if two important housing policies, luxury tax and actual price registration, help to depress the rising residential house prices. With the mega tick data of Taipei city for more than 80,000 residential house transaction records, we examined the factors influencing the actual house prices spanning the period from June 2008 through May 2014. We applied the least squares regression and the quantile regression in the model estimations for housing valuation. In addition, the megadata set is organized in time series and cross-section structures for five subdistricts and the whole Taipei city as well. The empirical results show that low mortgage rates have been the most significant factor for soaring housing prices in Taipei for the past decade. We estimate that a 1% increase in mortgage rates reduces housing prices from 5% to 17%. The actual price registration policy contributes to the decrease in housing prices by 4% to 29%. Housing policy implications are made based on our empirical findings.

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Section: Empirical Modelmentioning

confidence: 99%

House Prices, Mortgage Rate, and Policy: Megadata Analysis in Taipei

Chen

2018

Sustainability

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“…In the real estate field, multivariate spatial data models have been widely explored [1][2][3][4][5], while multivariate spatio-temporal data models have received relatively less attention. The hedonic regression model is used to make real estate valuations and to determine the characteristics that affect property prices [6].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Housing Price Variations Using Spatio-Temporal Data

2019

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This paper proposes a hedonic regression model to estimate housing prices and the spatial variability of prices over multiple years. Using the model, maps are obtained that represent areas of the city where there have been positive or negative changes in housing prices. The regression-cokriging (RCK) method is used to predict housing prices. The results are compared to the cokriging with external drift (CKED) model, also known as universal cokriging (UCK). To apply the model, heterotopic data of homes for sale at different moments in time are used. The procedure is applied to predict the spatial variability of housing prices in multi-years and to obtain isovalue maps of these variations for the city of Granada, Spain. The research is useful for the fields of urban studies, economics, real estate, real estate valuations, urban planning, and for scholars.

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“…In the presence of a sufficient amount of real estate data, the traditional statistical theory postulates a normal distribution for the real estate prices, consequently requiring the adoption of specific statistical measures applicable to the data population (e.g., mean, median, variance, standard error, standard deviation, etc.). However, in the real estate field, there is usually a low amount of detectable real estate data: this circumstance, together with the stratification processes of real estate markets, are conditions that do not satisfy the postulate of a normal distribution of the observed real estate prices [1][2][3][4][5][6][7][8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

“…The resampling methods applied to the real estate market allow appraisals with a small dataset to be implemented and the postulate of normal distribution of the real estate prices to be overcome and, thus, these methods are able to produce relevant improvements in the statistical-estimative analysis usually executable in this field [2][3][4][5][6][7][8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Resampling Techniques for Real Estate Appraisals: Testing the Bootstrap Approach

2018

Self Cite

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Applied to real estate markets analysis, the resampling methods aim to contribute to the knowledge growth of real estate market dynamics, overcoming the issues related to the data scarcity and operational limits of traditional statistical theory. Among resampling methods, the Bootstrap technique appears to be the most suitable for the interpretation of real estate phenomena. In this study, for residential properties located in Cosenza (Calabria Region, Italy), a Bootstrap approach has been used in order to determine the marginal prices of the real estate characteristics detected, comparing the results with those obtainable with a traditional Multiple Regression Analysis.

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Real Estate Appraisals with Bayesian Approach and Markov Chain Hybrid Monte Carlo Method: An Application to a Central Urban Area of Naples

Cited by 35 publications

References 59 publications

House Prices, Mortgage Rate, and Policy: Megadata Analysis in Taipei

House Prices, Mortgage Rate, and Policy: Megadata Analysis in Taipei

Estimation of Housing Price Variations Using Spatio-Temporal Data

Resampling Techniques for Real Estate Appraisals: Testing the Bootstrap Approach

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