Annex I. When Do Real Estate Prices Depart from “Fundamentals”?
1. The question of when and whether real estate prices are disconnected with “fundamentals” is a difficult question to answer.
2. Any definition of a “fundamental” is model-specific. A simple asset pricing model would define the “fundamental” price of a house as the present discounted value of all future rents that an investor receives (or avoids to pay) from owning the house. Expanding this model, in a hypothetical country where individuals migrate inter-state to search for better jobs, the value attached to the purchase of a house depends on how liquid the housing market is. In such a scenario, liquidity is another determinant of “fundamentals” that influence house prices.
3. In another version of the pricing model, if house buyers are credit constrained, then house prices might be lower than the present discounted value of rents. In this case, the extent to which credit constraints are present and binding would also constitute a “fundamental” determinant of house prices.
4. A somewhat more subtle scenario is when moral hazard is present in the market for real estate lending. Moral hazard arises when the government provides “guarantees”—implicit or explicit—to the banking sector. When these “guarantees” are present, the value of real estate would be higher than in the case where there is no guarantee.
5. This leads to an important caveat: the definition of “fundamental” depends on the choice of a model used and the variables that define the model. House prices may differ from levels predicted using a certain definition of “fundamental”, but that same level of prices may be perfectly explained when the definition of “fundamental” is expanded to include one or more variables. Thus, to avoid confusion, this paper uses the expression “predictor” to be consistent with the econometric nature of the analysis instead of “fundamental”.
Annex II. Model: Residential Real Estate Price-to-Income
Annex III. Model: Growth in Residential Mortgage Lending
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Prepared by Julian Chow (WHD).
Based on data from Numbeo which defines mortgage as percentage of income as a ratio of the actual monthly cost of the mortgage to take-home family income.
Numbeo defines price to income ratio as the ratio of median apartment prices to median familial disposable income, expressed as years of income (lower is less risky). Price to rent ratio is computed as the average cost of ownership divided by rent (lower values suggest that it is better to buy rather than rent, and vice versa).
Latin American countries comprise Argentina, Bolivia, Brazil, Chile, Colombia, Costa Rica, Mexico, Peru and Venezuela.
Numbeo defines mortgage-to-income as a ratio of the actual monthly cost of the mortgage to take-home family income.
While the authorities have created a price index of new housing (Indice de Precios de Vivienda Nueva (VNPI)), which includes Panama City and San Miguelito, it is used for internal monitoring and is not publicly available.
First time buyers of new homes receive preferential mortgage interest rate (two percentage points off the market rate) for residential real estate valued at US$180,000 and below.
Research shows these indicators together can predict a crisis as early as two to four years in advance (IMF, 2011a).
See the BCBS consultative document (http://www.bis.org/bcbs/publ/d307.pdf) proposing a range of risk weights (from 25 to 100 percent) driven by LTV and DSTI ratios.
In Panama, commercial banks are regulated by the Superintendency of Banks; insurance companies are regulated by the Superintendence of Insurance and Reinsurance; and cooperatives are supervised by the Panamanian Autonomous Institute for Cooperatives.
An alternative specification for the econometric model has also been explored with filtered real GDP growth, using the Hodrick-Prescott (HP) filter to remove short-term fluctuations associated with business cycles. However, the coefficient of this variable is not statistically significant.
The frequency of the source data for price to income ratio and population are semi-annual and annual, respectively.
Google is the largest global internet search engine, with a share of over 90 percent of search activity. The Google Trends data—available since January 2004 on a monthly basis—aggregate individual search queries on G according to terms, time, category and location based on the IP address from which the search is conducted. Stephens-Davidowitz and Varian (2014) provide further details on the construction of the Google Trends data.
See Cevik (2020, forthcoming), Narita and Yin (2018), Carrieré-Swallow and Labbé (2013).
Based on Engel-Granger test for cointegration and Breusch-Godfrey serial correlation LM.oogle according to terms, time, category and location based on the IP address from which the search is conducted. Stephens-Davidowitz and Varian (2014) provide further details on the construction of the Google Trends data.
Based on Engel-Granger test for cointegration and Breusch-Godfrey serial correlation LM.