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Investigating the effects of service and management on multifamily rents: a multilevel linear model approach

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Abstract

Unlike the large body of research on the determinants of single family prices and rents, the determinants of multifamily rents has received much less exploration. Using a recent and comprehensive micro-level dataset of multifamily housing units in Montgomery County, Maryland, USA, we applied a multilevel linear model with random coefficient to explore the determinants of multifamily rents, including the effects of service and management attributes. The findings are as follows: (1) first we find that a multilevel linear model is better suited to address datasets that include multiple apartment units in a smaller set of facilities, (2) for certain datasets—including ours–a random coefficients model outperforms both an OLS and random intercept model and (3) the effects of service and management variables on multifamily rents vary across types of service and management. Pet allowance, availability of short-term leasing options, and storage service availability increase rents significantly. Conversely, offering units to property employees and services to those with a disability decrease rents significantly.

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Notes

  1. The definition of a multifamily house varies by organization. The standard industry definition of multifamily housing is a structure with five or more units. According to this definition, multifamily housing is also generally considered to be renter-occupied housing, while owner-occupied condominiums are usually not considered to be multifamily housing units even though they may be located in multifamily structures.

  2. The number of households in multifamily rental housing comes from Multifamilybz.com. The definition of multifamily housing for this calculation is a structure with five or more units that is renter-occupied.

  3. Three-level MLM could be an alternative: Level 1 (unit level), Level 2(complex level), and Level 3 (neighborhood level). Level 3 could include a few explanatory variables. Two of them are school quality and census tract crime rate. The school district boundaries do not entirely contain boundaries of certain census tracts, which may complicate the analysis when we applied a three-level MLM. For simplicity and feasibility, this study utilizes a two-level MLM.

  4. We tried to include an explanatory variable indicating the distance to open space, and an explanatory variable indicating the distance to an elementary school in the model; however, VIF values of these explanatory variables are larger than 10. This suggests that there may be a multicollinearity problem if they are included in the model. As such, we do not include these two explanatory variables in the models.

  5. R2 is frequently used as an indicator for comparing models in terms of model fitness. However, Nakagawa and Schielzeth (2013) suggest that using R2 from traditional OLS linear model for MLMs yields misleading results and therefore should not be used. There are multiple ideas regarding how to compute R2 for MLMs For example, pseudo-R2, marginal R2, and conditional R2 could be used, but there is no consensus on this. In this study, we also use marginal R2 and conditional R2 to compare MLMs in terms of modeling fitting. Marginal R2 describes the proportion of variance explained by the fixed factor(s) alone, while conditional R2 describes the proportion of variance explained by both the fixed and random factors (Johnson 2014, Nakagawa and Schielzeth 2013). It is worth noting that marginal R2 and conditional R2 are not comparable with R2 in the traditional OLS context.

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Acknowledgements

We gratefully acknowledge helpful comments from Paul Elhorst, Ingmar Prucha, Casey Dawkins, and Andrew McMillan. We also thank Thomas Tippett and Chao Liu for their work in accessing part of the data.

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Authors and Affiliations

  1. Department of Urban Studies and Planning, University of Maryland, College Park, MD, 20742, USA

    Qiong Peng & Gerrit J. Knaap

  2. National Center for Smart Growth, University of Maryland, 1219D Preinkert Field House, College Park, MD, 20742, USA

    Qiong Peng & Gerrit J. Knaap

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  1. Qiong Peng
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  2. Gerrit J. Knaap
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Correspondence to Qiong Peng.

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Peng, Q., Knaap, G.J. Investigating the effects of service and management on multifamily rents: a multilevel linear model approach. J Hous and the Built Environ 36, 991–1009 (2021). https://doi.org/10.1007/s10901-020-09786-w

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