R. Kelley Pace
Professor Louisiana State University
- Baton Rouge LA
Dr. Pace's research interests center around the development of spatial statistical and spatiotemporal statistical techniques.
Areas of Expertise
Research Focus
Real Estate Economics & Spatial Econometrics
Dr. Pace’s research focuses on real-estate economics and finance, emphasizing spatial econometrics. He applies advanced spatial models to analyze housing prices, sales, mortgage performance, and commercial-property valuation, delivering data-driven insights that refine market analysis and guide investment and policy decisions.
Education
University of Georgia
Ph.D.
Real Estate
1985
College of Idaho
B.A.
Finance
1979
Media Appearances
How will businesses reopen after coronavirus restrictions? Post-Katrina era offers clues
NOLA online
2020-05-02
This does not bode well for working-class areas following COVID-19. “Like Katrina, businesses with poorer clientele will be hit harder,” Pace, a member of that study group and a professor of finance at LSU and director of its Real Estate Research Institute, predicted. Nor does it bode well for enterprises operating on the edge. “To the degree that disasters effectively fast-forward trends, many firms that were declining prior to the current crisis or had older ownership may choose not to reopen,” Pace wrote.
Articles
Modeling Spatial, Temporal, and Multivariate Autoregressive Dependence in Real Estate Prices
The Journal of Real Estate Finance and Economics2024
We use parallel processing and improved algorithms to search for the weights given to two locational coordinates as well as three non-locational dimensions to find multidimensional neighbors (previous in time) to fit a multidimensional STAR model. We find that the improvements allow for quick specification searches. The effect of the improved specifications is a material reduction in the standard deviation of the residuals.
Differential measurement error in house price indices
The Journal of Real Estate Finance and Economics2024
This article investigates measurement errors when using indices to model house prices over time. Our analysis, comparing index prices to actual transaction values, reveals that in many cases, widely-used indices display measurement errors correlated with the index values. Measurement error correlated with predictors constitutes “differential measurement error” at the level of the data generating process (DGP). We further explore the presence of differential measurement error within the context of mortgage lending. Our findings uncover substantial measurement errors in mortgage data, which not only diminish the predictive accuracy of models but also introduce notable biases in the coefficient estimates of variables.
Impacts of extreme weather events on mortgage risks and their evolution under climate change: A case study on Florida
European Journal of Operational Research2024
We develop an additive Cox proportional hazard model with time-varying covariates, including spatio-temporal characteristics of weather events, to study the impact of weather extremes (heavy rains and tropical cyclones) on the probability of mortgage default and prepayment. We compare the survival model with a flexible logistic model and an extreme gradient boosting algorithm. We estimate the models on a portfolio of mortgages in Florida, consisting of 69,046 loans and 3,707,831 loan-month observations with localization data at the five-digit ZIP code level.
Imputing Borrower Heterogeneity and Dynamics in Mortgage Default Models
The Journal of Real Estate Finance and Economics2024
The determinants of mortgage default have been an area of rising interest since the 2008 recession. There are two distinguishing features of mortgage default analysis. First, predictor variables are often only recorded at origination. However, many important variables such as credit scores vary over time. Second, there are omitted variables (such as borrower’s income and job security). If omitted variables are correlated with included regressors or if only origination values are used in a dynamic model, then biases may be present in econometric models for default risk.
The rank-size rule and challenges in diversifying commercial real estate portfolios
The Journal of Real Estate Finance and Economics2023
The strategy of geographically diversifying a portfolio of commercial real estate assets is an intuitive approach for risk management. However, due to high concentrations of these assets in major metropolitan areas, investors may face additional constraints in the portfolio optimization process. The rank-size rule, a log-linear relationship between city rank and size, provides one of the greatest empirical regularities in regional science. As such, it serves as a possible theoretical guide to the weights given to properties by location in a commercial real estate portfolio.
Affiliations
- Homer Hoyt Institute : Fellow
- American Real Estate Society
- American Real Estate and Urban Economics Association
- International Association of Assessing Officers
Research Grants
BP Oil Spill Grant
Louisiana State University
2010-2011
SES-0729259, SES0729264
National Science Foundation
2007-2011
Media
