PEMODELAN DAN ANALISA FAKTOR-FAKTOR YANG BERKONTRIBUSI TERHADAP PENJUALAN PROPERTI DI NYC MENGGUNAKAN REGRESI LINEAR

Abstract

The propery sales market in New York City (NYC) is among the most dynamic in the world, influenced by a wide range of factors. This study aims to identify and analyze the factors that influence property sales price in NYC using quantitative approach, employing Exploratory Data Analysis (EDA) and regression model. The data utilized in this reasearch is sourced from the NYC Property Sales Dataser available on Kaggle, which covers sales transactions over one year. The research process involves data preprocessing to address missing values and outliers, followed by EDA to uncover patterns and relationships between variables. Subsequently, regression modeling is applied to access the significant impact of variables such as borought, tacclass, buildingclass, and pre-war or post-war building status on sales prices. The findings reveal that properties located in Manhattan, classified under taxclass 2, and categorized as pre-war tend to have realtively high prices. Additionally, numerical variables such as building size show a significant correlation with sales prices. However, the developed regression model explains only 21,9 % of the variation in sales prices, indicating that other factors also influence these prices.