Researcher ORCID Identifier

0009-0001-3349-7020

Graduation Year

2023

Date of Submission

4-2023

Document Type

Campus Only Senior Thesis

Degree Name

Bachelor of Arts

Department

Economics

Reader 1

Nishant Dass

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Rights Information

© 2023 Sabrina Zhou

Abstract

In this paper, I examine the impact of remaining lease terms on single tenant triple net retail lease capitalization rates. A capitalization rate is the ratio of a property's net operating income to its purchase price and is a widely used metric for estimating potential investment returns. The study specifically focuses on Dollar General transactions from 2007-2021, a homogeneous group of stores that operate in similar markets with consistent store size and building quality. To conduct my analysis, I utilized a dataset of 1,195 transactions collected from a proprietary database of transaction data and supplemented with data from Co-Star and the Federal Reserve Economic Database. Previous studies have often neglected to include the variable of remaining lease despite its crucial role in pricing NNN assets. This variable is particularly important as it reflects the expected years of guaranteed payment that the owner can expect from the investment and, therefore, impacts the value of the real estate. My analysis finds that the number of years remaining on a lease at the time of sale has a significant effect on capitalization rate, with investors willing to pay a premium for NNN properties during periods of economic uncertainty, which confirms the flight to quality theory. Furthermore, the institutional quality of the buyer, such as a public REIT, insurance company or private equity firm, was found to increase the capitalization rate of NNN properties. By identifying the factors that impact capitalization rates, this research provides a more comprehensive understanding of the determinants of capitalization rates in the NNN retail real estate sector. My findings can help real estate practitioners and investors make better-informed investment decisions and develop more accurate valuations of NNN properties in the face of changing industry dynamics.

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