EFFICIENCY-BASED HOUSING ALLOCATION: LEVERAGING DEA WITH CCR MODEL FOR ENHANCED DECISION – MAKING IN REAL ESTATES

Abstract

This study introduces an innovative approach to optimizing house searches using Data Envelopment Analysis (DEA) with the Charnes, Kooper, and Rhodes (CCR) model. Utilizing live housing data from a diverse selection of properties, the research clusters houses based on price ranges and constructs efficiency frontier graphs to identify optimal housing allocations within each cluster. By quantifying the efficiency of houses, the DEA methodology provides a robust framework for nuanced comparisons and enhances decision-making in the real estate market. The project investigates operational efficiency through the implementation of DEA, a powerful tool for comparing the efficiency of multiple units under varying conditions. DEA uses an efficiency frontier to signify peak performance achievable with specific inputs and outputs, offering insights into inefficiencies and opportunities for process optimization. Our methodology requires a comprehensive matrix of inputs, outputs, and relevant components for sampled decision-making units (DMUs), configured with specific metrics and orientation to provide relative efficiency scores and operational benchmarks. Central to our analysis is the CCR model, which evaluates the efficiency of DMUs under the assumption of constant returns to scale, facilitating uniform comparisons and highlighting avenues for improvement. This approach aims to empower organizations by minimizing costs and maximizing benefits in various scenarios, such as goods transportation, service management, and process optimization. By considering a wide range of variables and potential conflicting goals, our study strives to enhance overall efficiency and inform decision-making, optimizing resource use and achieving high relative efficiency across diverse real-world contexts.