Liang Peng

Color portrait of Liang Peng

Professor of Risk Management

Department Risk Management
Office Address 340 Business Building
Phone Number 814-863-1046
Email Address pul16@psu.edu

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Education

Ph D, Economics, Yale University, 2002

M Phil, Economics, Yale University, 1999

MA, Economics, Yale University, 1999

MS, Business Administration, Renmin University, 1997

BS, Applied Mathematics, Renmin University, 1994

Courses Taught

FIN 455 – QUANT BUSINESS ANALYSIS (3)
This course provides students with working knowledge of some widely used quantitative methods, such as Monte Carlo simulations, t-tests, linear regressions, nonlinear regressions, regressions with dummy variables, and regressions with interacting explanatory variables, as well their applications in business. The course will focus on understanding and applying each method, but not on statistical theory or their proof. Monte Carlo simulations will be used to substitute for mathematical proofs. By the end of the course, students should understand the purposes of the above methods and how to use them to solve real estate, financial, marketing, and risk management problems. Students should also be able to interpret results in ways that are correct, insightful, and useful, should be aware of potential problems of each method, such as the omitted variable bias, multicollinearity, heteroskedasticity of regressions, and should know how to make corrections if these problems are present. Students should also have developed working knowledge of R, which is a programming language and software environment widely used by quantitative analysts. Students should know how to use R to conduct basic data manipulation, do simple Monte Carlo simulations, do t-tests, and run linear and non-linear regressions.

REST 575 – QUANT ANALYSIS (3)
The course provides students with working knowledge of some of the widely used quantitative methods and their applications in business, as well as using statistical analysis software to apply such methods for business analyses and decision-making. By the end of the course, students will understand the purposes of these methods and how to use them to solve real estate, financial, marketing, and risk management problems.- Students will be able to interpret results in ways that are correct, insightful, and useful.- Students will be aware of potential problems of each method and know how to make corrections if these problems are present.- Students will also have developed working knowledge of statistical analysis software widely used by quantitative analysts.

REST 596 – Individual Studies (Variable)
Creative projects, including nonthesis research, which are supervised on an individual basis and which fall ouside the scope of formal courses.

REST 890 – Colloquium (1)
Continuing, professionally oriented seminars that consist of a series of individual lectures by faculty, students, or outside speakers.

RM 475 – QUANT BUSINESS ANALYSIS (3)
This course provides students with working knowledge of some widely used quantitative methods, such as Monte Carlo simulations, t-tests, linear regressions, nonlinear regressions, regressions with dummy variables, and regressions with interacting explanatory variables, as well their applications in business. The course will focus on understanding and applying each method, but not on statistical theory or their proof. Monte Carlo simulations will be used to substitute for mathematical proofs. By the end of the course, students should understand the purposes of the above methods and how to use them to solve real estate, financial, marketing, and risk management problems. Students should also be able to interpret results in ways that are correct, insightful, and useful, should be aware of potential problems of each method, such as the omitted variable bias, multicollinearity, heteroskedasticity of regressions, and should know how to make corrections if these problems are present. Students should also have developed working knowledge of R, which is a programming language and software environment widely used by quantitative analysts. Students should know how to use R to conduct basic data manipulation, do simple Monte Carlo simulations, do t-tests, and run linear and non-linear regressions.

REST 590 – COLLOQUIUM (1)
Continuing seminars that consist of a series of individual lectures by faculty, students, or outside speakers.

REST 600 – Thesis Research (Variable)

RM 450 – Cont Iss R Est Mkt (3)
Historical performance, land use issues, market valuation, real estate development, public policy issues.

RM 497 – Special Topics (3)
Formal courses given infrequently to explore, in depth, a comparatively narrow subject which may be topical or of special interest.

R M 450 – Contemporary Issues in Real Estate Markets (3)
Historical performance, land use issues, market valuation, real estate development, public policy issues.

R M 496 – Independent Studies (3)
Creative Projects, including research and design, which are supervised on an individual basis and which fall outside the scope of formal courses.

Selected Publications

Peng L., Zhang L., "House Prices and Systematic Risk: Evidence from Micro Data." Real Estate Economics, vol. 49, no. 4, 2021, pp. 1069-1092, doi:10.1111/1540-6229.12277, doi.org/10.1111/1540-6229.12277
Peng L., Gang J., Zhang J., "Are Pricier Houses Less Risky? Evidence from China." Journal of Real Estate Finance and Economics, vol. 63, no. 4, 2021, pp. 662 - 677
Peng L., "Benchmarking Local Private Commercial Real Estate Returns: Statistics Meets Economics." Real Estate Economics, vol. 48, no. 4, 2020
Peng L., Thibodeau T., "Interest Rates and Investment: Evidence from Commercial Real Estate." Journal of Real Estate Finance and Economics, 2020, doi:10.1007/s11146-019-09699-8, link.springer.com/article/10.1007/s11146-019-09699-8
Peng L., Gang J., Thibodeau T., "Risk and Returns of Income Producing Properties: Core vs. Non-core." Real Estate Economics, vol. 48, no. 2, 2020
Peng L., Thibodeau T., "Idiosyncratic Risk of House Prices: Evidence from 26 Million Home Sales." Real Estate Economics, 2017
Peng L., "The Risk and Return of Commercial Real Estate: A Property Level Analysis." Real Estate Economics, 2016
Peng L., Thibodeau T., "Risk Segmentation of American Homes: Evidence from Denver." Real Estate Economics, vol. 41, no. 3, 2013
Peng L., Schulz R., "Does the diversification potential of securitized real estate vary over time and should investors care?." Journal of Real Estate Finance and Economics, vol. 47, no. 2, 2013, pp. 310-340
Peng L., Arsenault M., Clayton J., "Mortgage Fund Flows, Capital Appreciation, and Real Estate Cycles." Journal of Real Estate Finance and Economics, vol. 47, no. 2, 2013, pp. 243-265
Peng L., "Repeat Sales Regression on Heterogeneous Properties." Journal of Real Estate Finance and Economics, vol. 45, no. 3, 2012, pp. 804-827
Peng L., Goetzmann W., Yen J., "The Subprime Crisis and House Price Appreciation." Journal of Real Estate Finance and Economics, vol. 44, no. 1, 2012, pp. 36-66
Peng L., Thibodeau T., "Government Interference and the Efficiency of the Land Market in China." Journal of Real Estate Finance and Economics, vol. 45, no. 4, 2012, pp. 919-938
Peng L., Miller N., "The Economic Impact of Anticipated House Price Changes - Evidence from Home Sales." Real Estate Economics, vol. 39, no. 2, 2011, pp. 345-378
Peng L., Miller N., "House Prices and Economic Growth." Journal of Real Estate Finance and Economics, vol. 42, no. 4, 2011, pp. 522-541
Peng L., Clayton J., Miller N., "Price-Volume Correlation in the Housing Market: Causality and Co-movement." Journal of Real Estate Finance and Economics, vol. 40, no. 1, 2010, pp. 14-40
Peng L., Nicolosi G., Zhu N., "Do Individual Investors Learn from Their Trading Experience?." Journal of Financial Markets, vol. 12, no. 2, 2009, pp. 317-336
Peng L., Clayton J., MacKinnon G., "Time Variation of Liquidity in the Private Real Estate Market: An Empirical Investigation." Journal of Real Estate Research, vol. 30, no. 2, 2008, pp. 125-160
Peng L., Goetzmann W., "Estimating House Price Indexes in the Presence of Seller Reservation Prices." Review of Economics and Statistics, vol. 88, no. 1, 2006, pp. 100-112
Peng L., Miller N., "Exploring Metropolitan Housing Price Volatility." Journal of Real Estate Finance and Economics, vol. 33, no. 1, 2006, pp. 5-18
Peng L., "GMM Repeat Sales Price Indices." Real Estate Economics, vol. 30, no. 2, 2002, pp. 239-261
Peng L., Goetzmann W., "The Bias of the RSR Estimator and the Accuracy of Some Alternatives." Real Estate Economics, vol. 30, no. 1, 2002, pp. 13-39
Peng L., Goetzmann W., Ibbotson R., "A New Historical Database for the NYSE 1815 To 1925: Performance and Predictability." Journal of Financial Markets, vol. 4, no. 1, 2001, pp. 1-32

Editorships

Real Estate Economics Special Issue on Commercial Real Estate, Co-Editor, January to September 2024
Help edit a special issue on commercial real estate for Real Estate Economics.
Real Estate Economics Special Issue on Real Estate Investment Trusts, Co-Editor, (areuea.org), August 2017 - January 2018
Real Estate Economics, Associate Editor, (onlinelibrary.wiley.com/journal/15406229), July 2017 - Present
Journal of Real Estate Finance and Economics, Editorial Board, (link.springer.com/journal/11146), August 2014 - Present
Journal of Real Estate Research, Editorial Board, (pages.jh.edu/jrer/about/edboard.htm), August 2013 - Present