Basic Stochastic Framework for Designing and Understanding Real Estate Models

Abstract

The real estate market offers significant opportunities for financial success, yet navigating its complexities—such as tax benefits and regulatory influences—can be challenging. Stochastic processes play a critical role in minimizing risk and optimizing returns in this domain. Despite its historically stable growth, real estate remains underutilized, particularly among lower-income households, partly due to the limitations of traditional financial models like Black-Scholes. These models often struggle to account for the volatility, interest rate fluctuations, and government policies that influence real estate markets. This paper demonstrates the efficacy of integrating advanced stochastic models, such as the Heston model and jump diffusion models, to address these limitations. Additionally, we explore diverse methods for validating these models and highlight key considerations in their design to ensure greater accuracy and practical relevance. These insights aim to enhance the robustness of real estate modeling, providing a pathway for improved financial decision-making.