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by Vrinda Mittal ’22, Junjun Quan ’23, and Dhruv Singal ’23
The Paul Milstein Center for Real Estate at Columbia Business School was the host for the 10th anniversary Housing-Urban-Labor-Macro (HULM) conference, which took place virtually on September 10 and September 11, 2020. The conference was well attended with 80 participants and featured speakers from MIT Sloan, University of Chicago, Stanford University, Princeton University, Columbia University, University of Southern California, University of North Carolina, and IE University. They spoke on a broad range of important questions in the realm of real estate and urban economics, with topics ranging from house price dynamics, home ownership rates, choice of firm location, to credit cycles.
Daniel Greenwald presented “Do Credit Conditions Move House Prices?” in which he and Adam Guren address the key question of what the role of credit was in the housing boom and bust. They develop a tractable framework whose new feature is a flexible degree of segmentation between the home ownership and rental markets. The paper shows how to pin down that segmentation by comparing how the price-to-rent ratio responds to an identified credit shock to the response of the home ownership rates to that same credit shock. The much larger response of the price-to-rent ratio implies a strong degree of segmentation, and allows the authors to conclude that the expansion of mortgage credit supply in the 2000s caused a 30-40% boom in house prices.
Erica Jiang presented “Financing Competitors: Shadow Banks’ Funding and Mortgage Market Competition”. Using shadow bank data obtained via FOIA requests, Erica shows that shadow banks are more likely to obtain funding from banks with in-house mortgage origination, and that the cost of this funding is more expensive if the banks’ local origination market share is high. The author uses a structural model of traditional and shadow banks in both origination and funding markets, and shows that the synergy between two types of lending lowers banks’ cost of financing shadow banks in mortgage origination by 35%. In other words, banks are happy funding their competitors since they gain valuable information that benefits their own mortgage lending business.
In the US, home equity accounts for one-third of household net worth, but households cannot diversify. As an asset class, residential real estate is featured by a low level of liquidity and high intermediation fees (500-600 bps). Anthony Zhang from Chicago Booth presented his co-authored paper, “Liquidity in Residential Real Estate Market”, with Nadia Kotova from Stanford GSB. Their work helps us understand the relationship between two measures of liquidity for residential real estate, time on the market (TOM) and price dispersion (PD). They find that TOM and PD are counter-cyclical and seasonal, that is, both measures tend to be low in the housing boom but high in the bust, and decrease during the summer hot season. In their search-and-bargaining model, TOM and PD will comove positively in equilibrium when there is a change to the entry rate of buyers (liquidity supply shock); TOM and PD will comove negatively when sellers become more patient (liquidity demand shock). Their work highlights the frictions underlying the housing market and welfare implications for its participants.
It is very important to understand how households form expectations during housing booms and what factors drive the differences among households. Martin Schneider from Stanford GSB presented his joint work with Fabian Kindermann, Julia Le Blanc, and Monika Piazessi. With survey evidence from the recent German house price boom (since 2010), they show that, on average, households tend to underpredict local price growth. Renters expect a higher price growth than homeowners, hence renters make more accurate forecasts on average. However, renters show a wider range of forecasts than owners. The authors rationalize these empirical observations with a quantitative learning model. Renters and owners learn about the market in different ways: renters pay rents and are better-informed about the cash flow a house generates, whereas owners are better-informed about housing prices. As a result, renters make higher forecasts based on the high rent growth and recovery from the financial crisis. The model is helpful in quantifying how much mistaken beliefs can contribute to housing booms.
Urban and labor related issues in macro often come to the fore at times of increased public scrutiny of the government’s dealings with the private sector. Some prominent examples of this intricate relationship include the much publicized recent bidding war for Amazon’s HQ2. Cailin Slattery of Columbia Business School presented her research on studying subsidy competition for firms between various US states and cities, with a focus on trying to elucidate the social welfare connotations of such competition. The traditional understanding would lead us to believe that such subsidy competition is a zero-sum game involving just transfers of rents. Cailin’s carefully collected dataset of nearly 500 such subsidy competitions, and a robust auction model help to peel the layers of these opaque relationships. Her work concludes that such competition results in an increase of allocative efficiency. However all the excess rents accrue to the firms rather than the local government.
Lastly, Esteban Rossi-Hansberg from Princeton University delivered the keynote address at the conference. The talk was based on his ongoing work with Ezra Oberfield, Pierre-Daniel Sarte and Nicholas Trachter. Their paper addresses a central question in urban economics and industrial organization - how do firms decide how many plants to establish, where to establish them and of what size? While the general problem is usually a nearly intractable combinatorial problem, Esteban and co-authors solve a simpler problem --the limit case of a density choice problem-- and shows that it involves the exact same trade-offs in the general problem. That is, firms choose the density of plants, rather than the exact plant locations. The key tradeoff for the firms include fixed cost (rent) and managerial costs, balanced with transportation costs (being closer to the customer). Their model shows the counter-intuitive sorting result that more productive firms (larger firms) set up plants/outlets in denser markets and choose larger plants there. At the same time, smaller and less productive firms set up plants in sparser markets, and their plants are smaller. These predictions are borne out in the data.
Dhruv Singal is a third year PhD student in Finance at Columbia GSB. His research interests include macroeconomics and asset pricing. Specifically his current research approaches monetary economics from a rich data-driven framework. Prior to joining the program, he worked as a Research Associate at Adobe Research, where he did research on machine learning, data mining and digital marketing. He has published articles in various computer science conferences, spanning fields like program analysis, big data and computer vision.
Junjun Quan is a third-year finance PhD student at Columbia Business School. His research focuses on corporate finance, macroeconomics, and household finance. In one recent work, he examines the corporate governance practice of institutional investors. Also, he has been studying the welfare implications of excess corporate debt and household debt on households. He was a research specialist at UC Berkeley before joining Columbia. He received a Bachelor of Science in physics from Fudan University.
Vrinda Mittal is a 4th year PhD student in Finance and Economics at Columbia Business School. Her research focuses on financial intermediation, real estate, and household finance. She applies and develops tools from the industrial organization literature to address questions in these areas. Prior to Columbia, she worked at NERA Economic Consulting in 2016-2017. She received a Master of Finance from MIT Sloan School of Management in 2016, and a B.A. in Economics with Honors Distinction from Lady Shri Ram College for Women, University of Delhi in 2015.