Attributes influencing real-estate property prices: A nonparametric bootstrap regression utilizing web-scraped spatial data
Most people needed help to afford high-quality homes, creating a high demand level that is costly for low to middle-income households. This study aims to determine whether conventional housing features, population density, nearby landmarks, and elevation above sea level influence the real estate app...
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| Main Authors: | , , |
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| Format: | text |
| Language: | English |
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Animo Repository
2023
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| Subjects: | |
| Online Access: | https://animorepository.dlsu.edu.ph/etdb_math/33 https://animorepository.dlsu.edu.ph/context/etdb_math/article/1035/viewcontent/2023_Asuncion_Gonzaga_Yu_Attributes_influencing_real_estate_property_prices_Full_text.pdf |
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| Institution: | De La Salle University |
| Language: | English |
| Summary: | Most people needed help to afford high-quality homes, creating a high demand level that is costly for low to middle-income households. This study aims to determine whether conventional housing features, population density, nearby landmarks, and elevation above sea level influence the real estate appraisal in Metro Manila. Web Scraping was performed to gather geospatial data and MCMC monotone multiple regression for filling in missing values. Exploratory data analysis was first initialized to determine the counts of scraped properties per city as well as to visualize the distribution of prices via Python and QGIS. A total of 32 model variations were fitted using the fixed design bootstrapping regression, depending on the combination set of independent variables, followed by conducting a test for multicollinearity. Findings suggest that fast-food chains have negative effects on property prices while schools and banks have negative and positive effects in the case of the random design method. It has also been observed that fixed design is more accurate as compared with random design since standard errors are lower and the 95% percentile confidence intervals offer a narrower scale. Housing features, hospitals, schools, banks, ATMs, parking spaces, commercialized buildings per barangay, and property features within 1km including commercialized buildings between 1km and 5km significantly affect the property prices in Metro Manila. |
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