Philippe Thalmann, Milad Zarin-Nejadan's Construction and Real Estate Dynamics (Applied Econometrics PDF

By Philippe Thalmann, Milad Zarin-Nejadan

ISBN-10: 1403915458

ISBN-13: 9781403915450

ISBN-10: 1403990131

ISBN-13: 9781403990136

This assortment offers with a few fascinating monetary questions regarding building and genuine property, particularly how building organizations focus on the volatility of building; even if speculative bubbles or industry basics force these fluctuations; new ideas to foretell development call for; why the workplace marketplace led the hot actual property cycle; and even if rules is accountable for speculative habit.

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Additional info for Construction and Real Estate Dynamics (Applied Econometrics Association)

Sample text

A simple method allows to point out the existence of a bubble, by looking at the price at the end of the cycle. Indeed, Poterba’s analysis shows that in the case of a bubble, the price at the end of the cycle is not equal but inferior to what it was at the beginning, since the upsurge in prices led to the production of new housing without any relation to demand, finally resulting in lower prices. In other words, a bubble could be identified when the price at the end of the cycle can be observed.

However, it cannot be expected that rents and prices vary at the same rate. Indeed, there are market defects. First, rents are contractually determined by rental leases for three, six or nine years during which rents are most often indexed to the building price index (ICC, Cost of Construction Index). Then, rents can increase by more than the ICC only when new renters move in, and even then, under certain conditions. Therefore, leases act as a brake upon rent variations, upward as well as downward variations.

It is based on the Kalman filter and is called Structural Time Series Analysis. It is available as a PC package called STAMP-structural time series analyser, modeller and predictor (Harvey, 1989). Starting from the premise that an annual time series can be decomposed into two unobserved components, the trend and cycle, the basic statistical model takes the form: yt = µt + ψt + et (A-1) where yt is the observed variable, µt its trend, ψt its cycle and et a white noise error term. The trend and cyclical components are state variables which are allowed to vary stochastically with time, unlike the deterministic models of standard OLS regression methods.

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Construction and Real Estate Dynamics (Applied Econometrics Association) by Philippe Thalmann, Milad Zarin-Nejadan


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