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Mihaylo College of Business and Economics, CSU Fullerton
Paper Title TBD
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Market Distortions with Collusion of Agents
Abstract:
We investigate housing market distortions with collusion of agents. The agency problem where agents sell clients’ houses with price discounts while their own with price premiums is quite straightforward. However, the issue that agents collaborate with each other to further maximize their own interests is elusive. When agents collude, the resulting market distortions may even be worse than previous studies suggested. Indeed, this paper finds that the agency problem and market distortions are much more severe with agent collusion, as both the discounts associated with clients’ houses and the premiums with agents’ own homes become much larger when the two agents collude each other. |
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Amiyatosh Purnanandam
University of Michigan
Paper Title TBD
Did Banks Pay "Fair" Returns to Taxpayers on TARP?
Abstract:
| Financial institutions received billions of dollars from the U.S. Treasury in the form of preferred equity under the Troubled Asset Relief Program (TARP) in 2008. Investments were made during a bad state, but the repayments came in a relatively good time. Comparing TARP's realized returns to private market securities with similar or lower risk over the same time period, we show that the recipients paid considerably lower returns to the taxpayers than the benchmarks. Consequently, the recipient banks enjoyed a subsidy of over $50 billion. The ex-post renegotiation of TARP contract terms were beneficial to the recipients, and soon after the repayment banks increased dividend payout and CEO compensation. While we do not evaluate the net social benefit of TARP, our results challenge the oft-cited narrative that taxpayers made profits on TARP investments from a purely financial standpoint. |
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Neng Wang
Columbia University
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University of Colorado - Boulder
Paper Title TBD
The 2000s Housing Cycle With 2020 Hindsight: A Neo-Kindlebergerian View
Abstract:
| We re-examine the 2000s housing cycle with the benefit of a decade of additional data. With "2020 hindsight," the 2000s housing cycle is not a boom-bust but rather a boom-bust-rebound. At the city level, areas with the largest price increases during the boom had the largest busts but also the fastest growth after the trough in 2012 and as a result have had the largest price appreciation over the full cycle. A standard urban framework of house price growth determined by local income, amenities, and supply determinants fits the cross-section of city house price growth between 1997 and 2019. The implied long-run fundamental is correlated not only with long-run price growth but also with a strong boom-bust-rebound pattern. We interpret the episode in a neo-Kindlebergerian model where an asset cycle starts with an improvement in economic fundamentals, the stochastic trend growth of the "dividend" to living in a city. Agents learn about the trend by observing dividends but use diagnostic rather than rational updating. Diagnostic learning generates a boom from over-shooting of beliefs by home-buyers and lenders. A bust ensues when beliefs start to correct, exacerbated by a price-foreclosure spiral that drives prices below their long-run level. The rebound follows as prices converge to a path commensurate with higher fundamental growth. We calibrate the model to match the national boom-bust-rebound and show it also can account for the cross-city patterns. |
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Yildiray Yildirim
City University of New York
Paper Title TBD
Deep Learning for disentangling Liquidity-constrained and Strategic Default
Abstract:
| We disentangle liquidity-constrained default and the incentives for strategic default using Deep Neural Network (DNN) methodology on a proprietary Trepp data set of commercial mortgages. Our results are consistent during the severe Financial Crisis (2008) and the plausible economic catastrophe ensuing from COVID-19 pandemic (2020-2021). We retrieve the motive of default from observationally equivalent delinquency classes by bivariate analysis of default rate on Net operating income (NOI) and Loan-to-Value (LTV). NOI, appraisal reduction amount, prepayment penalty clause, balloon payment amongst others co-determine the delinquency class in highly nonlinear ways compared to more statistically significant variables such as LTV. Prediction accuracy for defaulted loans is higher when DNN is compared with other models, by increasing flexibility and relaxing the specification structure. These findings have significant implications for investors, rating agencies and policymakers. |
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Yongqiang Chu
University of North Carolina at Charlotte
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Federal Reserve Bank of New York
Paper Title TBD
Defragmenting Markets: Evidence from Agency MBS
Abstract:
| Agency mortgage-backed securities (MBS) issued by Fannie Mae and Freddie Mac have historically traded in separate forward markets. We study the consequences of this fragmentation, showing that market liquidity endogenously concentrated in Fannie Mae MBS, leading to higher issuance and trading volume, lower transaction costs, higher security prices, and a lower primary market cost of capital for Fannie Mae. We then analyze a change in market design—the Single Security Initiative—which consolidated Fannie Mae and Freddie Mac MBS trading into a single market in June 2019. We find that consolidation increased the liquidity and prices of Freddie Mac MBS without measurably reducing liquidity for Fannie Mae; this was in part achieved by aligning characteristics of the underlying MBS pools issued by the two agencies. Prices partially converged prior to the consolidation event, in anticipation of future liquidity. Consolidation increased Freddie Mac’s fee income by enabling it to remove discounts that previously compensated loan sellers for lower liquidity. |
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Dan McMillan
University of Illinois
Paper Title TBD
Measures of Vertical Inequality in Assessments
Abstract:
| Standard measures of vertical inequity suggest that assessments are regressive in the sense that high-priced properties are often assessed at lower rates than low-priced properties. Conventional measures of measuring vertical inequality include a simple descriptive statistic – the price-related differential – and measures based on regressions. We show that regression based procedures are seriously flawed, with a bias that tends to imply regressivity even when it is not present. To supplement the price-related differential, we propose three approaches that focus on the entire distribution of assessments rather than attempting to provide a single measure to characterize the entire assessment process. The first is to compare Gini coefficients for sales prices and assessments. These statistics directly measure vertical inequality by determining whether the distribution of assessments is not as skewed toward low-value properties as are sales prices. Second, we show that the Suits Index, which has been used to analyze tax progressivity, can be used to analyze whether assessments are progressive or regressive. The third approach is to test formally whether the distribution of log sales prices is statistically different overall from the distribution of log assessed values. We compute all of the measures using data on sales prices and assessments for 48 large central city counties. |
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