Dynamic Pricing for Real Estate

ArXiv ID: 2408.12553 “View on arXiv”

Authors: Unknown

Abstract

We study a mathematical model for the optimization of the price of real estate (RE). This model can be characterised by a limited amount of goods, fixed sales horizon and presence of intermediate sales and revenue goals. We develop it as an enhancement and upgrade of the model presented by Besbes and Maglaras now also taking into account variable demand, time value of money, and growth of the objective value of Real Estate with the development stage.

Keywords: Revenue Management, Optimization Model, Time Value of Money, Variable Demand, Stochastic Control, Real Estate

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 3.0/10
• Quadrant: Lab Rats
• Why: The paper is highly mathematical, featuring advanced optimization, dynamic programming, and proofs of optimality for novel extensions to the Besbes and Maglaras model. However, it lacks empirical implementation, relying on theoretical algorithms and basic historical demand simulations without backtesting, code, or real-world data.

  flowchart TD
    A["Research Goal: Dynamic Pricing<br>Optimization for Real Estate"] --> B["Methodology: Enhancing<br>Besbes & Maglaras Model"]
    
    B --> C["Key Inputs & Parameters<br>Variable Demand, Time Value of Money,<br>Stochastic Control, Revenue Goals"]
    
    C --> D["Computational Process<br>Solver for Stochastic Optimization<br>Dynamic Programming"]
    
    D --> E{"Outcomes"}
    
    E --> F["Pricing Strategy<br>Optimal Price Trajectories"]
    E --> G["Performance Gains<br>Revenue Increase &<br>Value Appreciation Modeling"]