–Nonrecourse Mortgage and Housing Price Boom, Bust, and Rebound

Bao, Te; Ding, Li

doi:10.1111/1540-6229.12116

“…They turn out to be a powerful and flexible tool to study subjects' expectation formation under different feedback systems. Many LtFEs have been conducted to study how agents form their short-run expectations in financial markets (Hommes et al [2005]), real estate markets (Bao and Ding [2015]), commodity markets (Bao et al [2013]) and in simple macroeconomic frameworks (Assenzaa et al [2011], Anufriev et al [2013a], Cornand et al [2013]). We conduct a Learning to Forecast Experiment (LtFE) in which, unlike the standard settings 1 , subjects should submit a prediction for the asset price at different time horizons.…”

Section: Introductionmentioning

confidence: 99%

Long-run expectations in a learning-to-forecast experiment: a simulation approach

Colasante

¹

,

Alfarano

²

,

Camacho-Cuena

³

et al. 2018

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In this paper, we elicit both short and long-run expectations about the evolution of the price of a financial asset by conducting a Learning-to-Forecast Experiment (LtFE) in which subjects, in each period, forecast the asset price for each one of the remaining periods. The aim of this paper is twofold: on the one hand, we try to fill the gap in the experimental literature of LtFEs where great effort has been made in investigating short-run expectations, i.e. one step-ahead predictions,while there are no contributions that elicit long-run expectations. On the other hand, we propose an alternative computational approach with respect to the Heuristic Switching Model (HSM), to replicate the main experimental results. The alternative learning algorithm, called Exploration-Exploitation Algorithm (EEA), is based on the idea that agents anchor their expectations around the last market price, rather than on the fundamental value, with a range proportional to the recent past observed price volatility. Both algorithms perform well in describing the dynamics of short-run expectations and the market price. EEA, additionally, provides a fairly good description of long-run expectations. AbstractIn this paper, we elicit both short and long-run expectations about the evolution of the price of a financial asset by conducting a Learning-to-Forecast Experiment (LtFE) in which subjects, in each period, forecast the the asset price for each one of the remaining periods. The aim of this paper is twofold: on the one hand, we try to fill the gap in the experimental literature of LtFEs where great effort has been made in investigating short-run expectations, i.e. one step-ahead predictions, while there are no contributions that elicit long-run expectations. On the other hand, we propose an alternative computational approach with respect to the Heuristic Switching Model (HSM), to replicate the main experimental results. The alternative learning algorithm, called Exploration-Exploitation Algorithm (EEA), is based on the idea that agents anchor their expectations around the last market price, rather than on the fundamental value, with a range proportional to the recent past observed price volatility. Both algorithms perform well in describing the dynamics of short-run expectations and the market price. EEA, additionally, provides a fairly good description of long-run expectations.JEL: D03 G12 C91

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“…They turn out to be a powerful and flexible tool to study subjects' expectation formation under different feedback systems. Many LtFEs have been conducted to study how agents form their short-run expectations in financial markets (Hommes et al [2005]), real estate markets (Bao and Ding [2015]), commodity markets (Bao et al [2013]) and in simple macroeconomic frameworks (Assenzaa et al [2011], Anufriev et al [2013a], Cornand et al [2013]). We conduct a Learning to Forecast Experiment (LtFE) in which, unlike the standard settings 1 , subjects should submit a prediction for the asset price at different time horizons.…”

Section: Introductionmentioning

confidence: 99%

Long-run expectations in a learning-to-forecast experiment

Colasante

¹

,

Alfarano

²

,

Camacho-Cuena

³

et al. 2017

Applied Economics Letters

View full text Add to dashboard Cite

In this paper, we elicit both short and long-run expectations about the evolution of the price of a financial asset by conducting a Learning-to-Forecast Experiment (LtFE) in which subjects, in each period, forecast the the asset price for each one of the remaining periods. The aim of this paper is twofold: on the one hand, we try to fill the gap in the experimental literature of LtFEs where great effort has been made in investigating short-run expectations, i.e. one step-ahead predictions, while there are no contributions that elicit long-run expectations. On the other hand, we propose an alternative computational approach with respect to the Heuristic Switching Model (HSM), to replicate the main experimental results. The alternative learning algorithm, called Exploration-Exploitation Algorithm (EEA), is based on the idea that agents anchor their expectations around the last market price, rather than on the fundamental value, with a range proportional to the recent past observed price volatility. Both algorithms perform well in describing the dynamics of short-run expectations and the market price. EEA, additionally, provides a fairly good description of long-run expectations. JEL: D03 G12 C91

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“…This finding is attributable to asset prices that have to drop deep enough to take out the prior price rise resulting from under-priced default risk, as well as capture the new supply and demand. Bao & Ding (2016) examine the influence of non-recourse and recourse loans on housing price dynamics in major U.S. cities between 2000 and 2013. Consistent with the finding of Pavlov & Wachter (2009), they find that housing price in states exhibiting non-recourse mortgages experiences a deeper drop than it is in recourse states.…”

Section: Literature Reviewmentioning

confidence: 99%

The Impact of Under-Pricing of Default Risk on Investment: Evidence from Real Estate Investment Trusts (REITs)

Steininger¹,

Nguyen²

2019

26th Annual European Real Estate Society Conference

0

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Under-pricing the default risk of borrowers is inevitable in a market with many lenders because of a race to the bottom of lending rates. This study examines the impact of under-priced default risk on investment in the real estate investment trust (REIT) sector where firms' investment is highly sensitive to changes in credit market conditions. The findings reveal that REITs exploiting under-priced default risk have a higher level of investment than their peers because the former could obtain access to loans having low rates of interest. Moreover, exploiting the under-priced default risk is specific to not only REITs but also the whole real estate investment sector. In contrast, under-priced default risk appears to have an insignificant impact on investment decisions of industrial firms. It is attributable to their total assets that do not have much real estate assets to make non-recourse loans.

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–Nonrecourse Mortgage and Housing Price Boom, Bust, and Rebound

Cited by 14 publications

References 30 publications

Long-run expectations in a learning-to-forecast experiment: a simulation approach

Long-run expectations in a learning-to-forecast experiment: a simulation approach

Long-run expectations in a learning-to-forecast experiment

The Impact of Under-Pricing of Default Risk on Investment: Evidence from Real Estate Investment Trusts (REITs)

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