Where to Build, What to Build, and Why: An Empirical Framework for Optimal Luxury Hospitality and Short-Term Rental Development and Investment

Abstract

In recent years, short-term rental platforms such as Airbnb and Vrbo have transformed the global hospitality market, underscoring the importance of understanding what drives performance in this sector. This thesis investigates how neighborhood-level characteristics influence short-term rental performance, moving beyond property-level analysis to develop a deeper, location-based investment framework for short-term rental investors. This question is important from an economic perspective, as identifying what neighborhood characteristics drive success can inform more efficient zoning rules, permit caps, and other regulatory policies. From an investment perspective, this paper uses econometric analysis to provide a framework for evaluating the location of potential short-term rental acquisitions. Using a panel dataset from AirDNA, the U.S. Census Bureau’s American Community Survey (5-Year), and DataAxle’s Business Academic dataset from 2014-2024, I estimate a series of ordinary least squares regressions with year-fixed effects across multiple performance outcomes, including revenue, average daily rate (ADR), occupancy rate, and short-term rental supply. The results suggest that the most important performance driver is a zip code’s population density. Experience-oriented amenities, such as tourist attractions and entertainment, are also significant positive drivers. In contrast, tourist service amenities, golf courses, and larger properties (proxied by the number of bedrooms) have significant negative effects. Finally, the presence of hotels suggests the coexistence of both agglomeration benefits (reflected in higher revenue and ADR) and competition (reflected in lower occupancy rates and demand). These findings contribute to the literature by breaking down “location” into measurable amenity categories that provide actionable insights for investors and policymakers to identify markets most conducive to short-term rental performance.