The Paris Residential Market: Driving Factors and Market Behaviour 1973-2001

In this paper we investigate the driving factors associated with the Paris apartment market. We explore a database of nearly 230 000 transactions for residential properties in the Paris area over the 1973 – 2001 period. We develop a factorial model that may capture the systematic link between residential prices and a set of predefined economic variables or a linear combination of these economic variables. We assume that capital growth rates in real estate are related to the variables we defined in the last paragraph. We measure this link which underlines the ‘true path’ of the real estate market: in that way we can develop an index as a function of many other indices. The methodology we develop, based on a multifactor approach to apartment price movements in the long run, has two main advantages over traditional indices. Firstly, we are able to identify the main driving factors for the Paris residential market. And secondly, the factors thus derived can be used to generate a “factor model” useful in comparison to existing capital growth indices and that provides valuable intuition for forecasting residential prices.Real estate indexes; Repeat sales; Risk factors

Which Capital Growth Index for the Paris Residential Market?

In this paper we address the issue of measuring price performance for the Paris residential market. Our main focus is on choosing the appropiate index or indices capable of efficiently capturing capital growth, capital risk, and identifying the main risk factors inherent in this specific market.We identifying three existing indices but show that they may not be completely appropriate to address our main goals. We therefore construct two complementary repeat sales indices: a Case & Shiller (1987) Weighted Repeat sales (WRS) index and a Factorial index using the Baroni, Barthélémy & Mokhrane (2001) approach. We use the CD-BIEN database that contains more than 220 000 repeta sales transactions for residential properties in Paris area covering the period 1983-2001 period.We estimate these two indices for the Paris and close surrounding area and compare them to different existing indices: (I) the square metre index provides by the Chambre des Notaires de Paris and INSEE, (II) the IDP indices, (III) the listed real estate index. OUR conclusions yield interesting implications concerning real estate risk and suggest the construction of jointly using the repeat sales and the factorial approachesReal estate indexes; valuation-based index; repaet sales; risk factors

Optimal holding period In Real Estate Portfolio

This paper considers the use of simulated cash flows to determine the optimal holding period in real estate portfolio to maximize its present value. The traditional DCF approach with an estimation of the resale value through a growth rate of the future cash flow does not let appear this optimum. However, if the terminal value is calculated from the trend of a diffusion process of the price, an optimum may appear under certain conditions. Finally we consider the sensitivity of the optimal holding period to the different parameters involved in the cash flow estimations. This methodology may be applied in commercial valuation and enables to get an optimal holding period for a given portfolio.valuation, DCF, optimal holding period, commercial property

Is it possible to construct derivatives for the Paris residential market?

In this paper we address the issue of the robustness of the price level, mean, and variance estimates for two sets of repeat sales real estate price indices: the classical WRS method and a PCA factorial method, as elaborated in Baroni, Barthélémy and Mokrane (2007). Our work can be seen as an extension of Clapham, Englund, Quigley and Redfearn (2006), with the aim of helping to judge of the efficiency of such indices in designing real estate derivatives contracts. We use an extensive repeat sales database for the Paris (France) residential market. We describe the dataset used and compute the parameters (drift and volatility) of the indices produced over the period 1982- 2005. The aim here is to test the sensitivity of these two indices to revision due to additional repeat-sales transactions information. Our analysis is conducted on the global Paris market and on submarkets. Our main conclusion is that the revision problem may cause serious concern for the stability of key parameters that are used as inputs in the pricing of derivatives contracts. The impact of index revision is important on the estimate of the index price level. This result is consistent with the finding of the existing literature for the US and Swedish markets. We also find that although the revision impact on the trend estimate can be important, the WRS method seems more robust and derivatives contracts such as swaps may be based on such indices. Finally, and this is probably the most promising result, revision influence on volatility estimates seems to be less stringent, and according to the robustness of the volatility estimate, the BBM factorial index seems to fare relatively better than the WRS index. Hence, we find that the factorial index could better sustain volatility based derivatives such as call or put options.

Optimal Holding Period for a Real Estate Portfolio

This paper considers the use of simulated cash flows to determine the optimal holding period of a real estate portfolio to maximize its present value. The traditional DCF approach with an estimation of the resale value through a growth rate of the future cash flow does not let appear this optimum. However, if the terminal value is calculated from the trend of a diffusion process of the price, an optimum may appear under certain conditions. Finally we consider the sensitivity of the present value to the different parameters involved in the cash flow estimations.Cash Flows Simulations; Holding Period; Real Estate Portfolio Management

A repeat sales index robust to small datasets

As suggested by D. Geltner, commercial properties indices have to be built using repeat sales instead of hedonic indices. The repeat sales method is a means of constructing real estate price indices based on a repeated observation of property transactions. These indices may be used as benchmarks for real estate portfolio managers. But the investors in general are also interested in introducing real estate performance in their portfolio to enhance the efficient frontier. Thus, expected return and volatility are the two key parameters. To create and to improve contracts on real estate indices, trend and volatility of these indices must be robust regarding to the periodicity of the index and the volume of transactions. This paper aims to test the robustness of the trend and volatility estimations for two indices: the classical Weighted Repeat Sales (Case & Shiller 1987) and a PCA factorial index (Baroni, Barthélémy and Mokrane 2007). The estimations are computed from a dataset of Paris commercial properties. The main findings are the trend and volatility estimates are biased for the WRS index and not for the PCA factorial index when the periodicity increases. Consequently, the level of the index at the end of the computing period is significantly different for various periodicities in the case of the WRS index. Globally, the PCA factorial seems to be more robust to the number of transactions. Firstly, we present the two methodologies and then the dataset. Finally we test the impact of the number of transactions per period on the trend and volatility estimates for each index and we give an interpretation of the results.Repeat sales indices, Index estimations, Transactions volume

A PCA Factor Repeat Sales Index (1973-2001) To Forecast Apartment Prices in Paris (France)

In this paper we address the issue of building a repeat sales index, based on factors. This is an extension of a companion paper, Baroni, Barthélémy and Mokrane (2001, BBM) in which we had built a factorial index as a selected linear function of existing economics and financial variables. Here we offer a more general and robust model based on a Principal Components Analysis (PCA). We apply this methodology to the Paris residential market. We use the CD-BIEN database that contains more than 220 000 repeat sales transactions for residential apartments in the Paris area covering the period 1973-2001 period. Our PCA index for the Paris and close surrounding area is estimated and its characteristics and robustness are analysed depending on: estimation period, choice of observations, periodicity and reversibility. We then compare it to the traditional WRS repeat sales index developed by Case & Shiller (1987). Finally we show that contrary to the WRS index, our index can be used to forecast apartment prices.Real estate indices; Repeat sales; Factors; PCA; Index forecasting

A Repeat Sales Index Robust to Small Datasets

As suggested by D. Geltner, commercial properties indices have to be built using repeat sales instead of hedonic indices. The repeat sales method is a means of constructing real estate price indices based on a repeated observation of property transactions. These indices may be used as benchmarks for real estate portfolio managers. But the investors in general are also interested in introducing real estate performance in their portfolio to enhance the efficient frontier. Thus, expected return and volatility are the two key parameters. To create and to improve contracts on real estate indices, trend and volatility of these indices must be robust regarding to the periodicity of the index and the volume of transactions. This paper aims to test the robustness of the trend and volatility estimations for two indices: the classical Weighted Repeat Sales (Case & Shiller 1987) and a PCA factorial index (Baroni, Barthélémy and Mokrane 2007). The estimations are computed from a dataset of Paris commercial properties. The main findings are the trend and volatility estimates are biased for the WRS index and not for the PCA factorial index when the periodicity increases. Consequently, the level of the index at the end of the computing period is significantly different for various periodicities in the case of the WRS index. Globally, the PCA factorial seems to be more robust to the number of transactions. Firstly, we present the two methodologies and then the dataset. Finally we test the impact of the number of transactions per period on the trend and volatility estimates for each index and we give an interpretation of the results.Index Estimations ; Property Transactions Volume ; Repeat Sales Indices

Physical Real Estate: A Paris Repeat Sales Residential Index

In this paper we present the repeat sales index methodology developed by Case and Shiller (1987) and its estimation problem. We particularly describe the problem arising from the time intervals construction for the estimation. We then apply this methodology to the Paris residential market. We use the CD-BIEN database that contains more than 220 000 repeat sales transactions for residential properties in the Paris area covering the period 1973-2001 period. This index based on returns is compared to the official one used in France for Paris based on single prices, the Notaires/INSEE index. We then underline the robustness of in the index estimation according to its periodicity by the way of the return and volatility estimation. The index sensibility to the time period is studied in the last part. We conclude that i) the estimation is quite robust whatever the estimation period is, and ii) this index is significantly different from the official residential index for ParisReal estate indexes; Repeat sales indexes

Monte Carlo Simulations versus DCF in Real Estate Portfolio Valuation

This paper considers the use of simulated cash flows to value assets in real estate investment. We motivate the use of Monte Carlo simulation methods for the measurement of complex cash generating assets such as real estate assets return distribution. Important simulation inputs, such as the physical real estate price volatility estimator, are provided by results on real estate indices for Paris derived in an article by Baroni, Barthélémy and Mokrane (2005). Based on a residential real estate portfolio example, simulated cash flows (i) provide more robust valuations than traditional DCF valuations, (ii) permit the user to estimate the portfolio’s price distribution for any time horizon, and (iii) permit easy Values-at-Risk (VaR) computations.DCF; Monte-Carlo Simulations; Real Estate Indices; Real Estate Valuations