Abukar Warsame (p), Mats Wilhelmsson (p)

One of the main characteristics of derived demand is that it can create a ripple effect on local economy and industries. It can also be affected by regional and national socio-economic factors such as employment and education levels, income and type of industries. Real estate demand is a good example of derived demand where house price as principal driving force of housing demand is mainly affected by the income levels generated by the different employment categories.
An increase/decrease of certain type of employment in a region or local community over certain period of time is expected to have impact on house prices through change of income. Shift-share analysis could be useful tools to explore the extent and intensity of employment changes in different industries. They can also illuminate if the employment changes were mainly due to regional or national competitiveness of specific type of industry. However, these techniques were often used in determining economic multipliers but not in real estate economics research especially in relation to house price and real estate demand studies.
The aim of this paper is to utilize shift-share analysis in order to estimate the change of employment levels of sixteen industry categories between 2000 and 2015 and across 290 municipalities. This would allow us to explore with a fixed /random effect spatial model if the observed changes of employment levels has an impact on house prices and subsequently housing demand of corresponded municipalities and regions during the study period. In addition we are also utilizing shift-share analysis on the distribution of income in the municipality.
The shift-share analysis outcome indicates prevalence of certain specific type of employment and income groups in different regions. The expectation is that this comparative advantage both in employment and income will induce increase of housing demand and house prices in these regions.

Benoit Lefebvre, Arnaud Simon (p), Alain Coën, Raphael Languillon

Since the financial crisis, Central banks have used unconventional monetary policies and increased the quantity of money available in the economy. The purpose of this paper is to study the effect of the global monetary policies on the commercial real estate market. Indeed, office prices have known a significant rise in most of the European markets, mostly explained by an increase of the demand side, and we could suspect an effect of the monetary policies. The paper uses a panel estimation to determine the relationship between office price indexes and explanatory variables, both at different locations and at different period. The panel analysis allows understanding price movements across 16 office markets in Europe between 2009 and 2016. To pick-up the relation between prices and money supply, we have constructed for each market a monetary index adapted to commercial real estate. Results show that office prices are driven both by sectorial and economic variables. It also exists a positive relationship between the global monetary supply and the office prices. Moreover, we show that a metropolis will be more impacted by the massive injection of liquidity of Central banks. By looking more closely at the data, we show also that there is mostly a difference between German markets and the rest of Europe. Finally, we show that, even if the unconventional monetary policies were made essentially to support peripheral markets, the main beneficiaries are those of the European megalopolis.

Lars Vandrei (p)

We analyze to what extend sales prices for residential housing prices react towards rent-price regulation. We do this exploiting a quasi-natural design in the German federal state of Brandenburg while using actual transaction price data, provided by the committee of evaluation experts. Brandenburg announced and introduced both a capping limit for existing rental contracts as well as a price ceiling for new contracts for municipalities with "tight housing markets" in 2014. Whether or not a municipality falls under this classification is based upon a region's housing data that is translated into a specific score that ranges from 0 to 100. The regulations were introduced in municipalities with scores of above the average plus two standard deviations. We exploit this sharp cut-off point in a regression discontinuity design. First, we standardize prices in a hedonic regression model. Then we compare prices in regions that are located marginally above this threshold with prices in those slightly below. The analysis contains 15 municipalities in the treatment and 20 in the control group. An analysis of media citations shows that the public discussion of the two instruments did not start before the year 2013. We therefore compare a time frame before 2013 with one after 2014 to exclude anticipation effects. We expect to find no significant effects when we use a time-frame prior to media coverage of the regulations. When people are generally informed, however, apartment prices should be lower in regulated regions.