The dissertation focuses on developing a broad conceptual framework to study the impact of property transactions and amenities on property prices in Singapore, using data from 1990 to 2023 and covering 72 types of amenities. Regression analysis, including Pearson's R and Lasso Regression, was conducted alongside prediction models using LASSO, Random Forest, and LSTM, demonstrating that including a wider range of amenities improves prediction accuracy over traditional methods.

Singapore's dense population and high real estate demand make the relationship between amenities and property prices especially relevant. Previous studies have typically focused on a few amenities, like proximity to schools or metro stations, but this research aims to provide a comprehensive analysis by considering a wide array of amenities sourced from OpenStreetMap.

The methodology involved combining data from various sources, including primary schools, bus stops, MRT stations, malls, and other amenities, with property transaction data for both public and private housing. The research highlighted the importance of amenities in property valuation and demonstrated through machine learning models that a more detailed amenity inclusion significantly enhances property price prediction accuracy.

In terms of managerial implications, this study underscores the necessity of incorporating a diverse range of amenity types into real estate market analyses, particularly in dense urban environments like Singapore. The findings could assist consumers, businesses, and government agencies in better predicting property prices in new locations and contribute to a more accurate market model.

The research acknowledges limitations related to computational resources and suggests future studies could explore the specific effects of individual amenities, incorporate socio-economic metrics for future value predictions, and examine how amenities influence different housing types in Singapore. Overall, the dissertation represents a significant step forward in understanding the complex dynamics between property prices and amenities, providing a valuable resource for future research in the field.