Apartment Prices, State of the Economy and Periods Suspicious of Redlining
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Apartment prices, state of the economy and periods suspicious of redlining: A comparison of a ‘good’ and ‘bad’ standing Rotterdam neighborhood Author: A.L. de Visser Student ID: 287888av Date: January 16, 2013 Section: Economics & Business Supervisor: Dr. A. Otgaar Apartment prices, state of the economy and periods suspicious of redlining: A comparison of a ‘good’ and ‘bad’ standing Rotterdam neighborhood By A.L. (André) de Visser A Master thesis at the Rotterdam Erasmus School of Economics Department of Economics & Business Rotterdam, January 16, 2013 Abstract This research is an investigation about the relationship of the apartment prices development in two contrasting Rotterdam neighborhoods and the general Dutch economical circumstances. Examined is the price development in the two contrasting neighborhoods during the economical business cycle and its distinct phases. The research period is 1995-2011. For each investigated neighborhood, a weighted repeated sales index is constructed for this period. All this is done in order to come to an answer to the research question: Is there a relationship between the social status of city neighborhoods and the development of apartment prices, while considering the state of the economy? These constructed apartment price indexes are also used to examine the suspicious of redlining for Rotterdam. For this purpose, also a counting table of number of transactions for each neighborhood is developed. The outcome of this research is that apartment prices in both the ‘good’ and ‘bad’ standing neighborhood show the same development, in each distinct general economic circumstance. The suspicious of redlining is not supported by the data used for this investigation. Keywords: apartment price, real estate, housing, redlining, economic circumstances, recession, upturn, real GDP, the Netherlands, Rotterdam, neighborhood, good standing, bad standing, weighted repeated sales index. i Management summary In this summary, the steps followed throughout this thesis are set out. This for reasons of the complexity of this research and for getting a good overview. This summary is divided into a part about the existing literature and a part about the method section. 1 Existing literature part: Firstly, the real estate and apartment market is described. Apartment price determinants and the formation of prices is explained. This to give the reader a good insight on how the apartment transaction prices are realized. Then, the urban apartment market is handled. In this urban market, social status and redlining play an important role. Social status divides neighborhoods into ‘good’ and ‘bad’ standing, which will be thoroughly elaborated. The for this thesis investigated neighborhood of ‘bad standing’ is suspicious of redlining during certain periods and therefore this phenomenon is thoroughly discussed. Because for this thesis the apartment price development is linked to the development of real GDP, the business cycle and especially its various phases are deeply discussed. The same counts for the existing literature about the apartment price development during these distinct phases. Further, an index to measure the apartment price development over time can be constructed in multiple ways, and therefore these indexes with their pro’s and con’s are discussed. 2 Method section part: In this section, the difference between the database of the NVM and the Dutch Land Registry Office (DLRO) is handled. For this thesis, there is chosen to use the database of the DLRO. This because this database contains the whole housing sales market to private persons, auction sales (forced by for example banks) included. Also this database is the most reliable in my view, as the data comes straight from notaries. The suspicious periods of Redlining for Rotterdam are as exactly as possible appointed. This because in most neighborhoods of ‘bad standing’, every now and then this phenomenon is present. Redlining is performed by banks, and whole postal code areas are excluded for mortgage supply. Therefore, the expectation is that Redlining has a negative impact on the development of apartment prices. Hereafter, the business cycle for the Netherlands is investigated by looking at real GDP over time. Peaks and troughs in the Dutch real GDP sequence are determined, these points of change form the beginning points of recessions and economic upturns in the business cycle (the general economy). Next to the whole business cycle, these distinct phases are important for this research. The aim is to investigate the development of apartment prices during the whole business cycle and also apart during the periods of recession and economic upturn. This to get a good picture of the price development in these distinct underlying periods of the overall development. ii The development of apartment prices in a ‘good’ and a ‘bad standing’ Rotterdam neighborhood during former mentioned periods are compared. In the neighborhood selection process, all following issues play a role: - the distance from the city center, - scores on social status indicators, - the relative presence of transactions under special conditions, and - the presence of certain types of apartments of a certain construction period. In the end, representative streets in both neighborhoods are selected. The 6 digit postal codes of these streets are used to do the request for data at the DLRO. The acquired data is explored, and because DLRO data contains little data of the apartments itself a weighted repeated sales index is constructed for each neighborhood. Transactions with very high relative price changes are removed, the same counts for transactions with very low price levels. For apartments with changes in between the transactions, the deviating transactions are removed. Sales transaction pairs are formed, and an apartment with more than 2 transactions during 1995-2011 is divided in multiple sales pairs. The periods of the indexes that are constructed, are as good as possible aligned to the turning points in the Dutch real GDP sequence. In this way, the apartment price development can be investigated over the individual periods of recession/upturn. The constructed indexes have periods of 1 year, while a more detailed index would become too thin. The transaction dates of the DLRO data is brought forward with one quarter, because of a lag of the moment of actual sales to the moment of transaction at the notary (which is noted in the DLRO database). In the weighted repeated sales index, the holding period between the transactions is taken into account. With the construction of such an index, 3 steps are involved. In the first step, the original repeated sales index is constructed, in SPSS. In the second step, the residuals of the sales pairs from the first regression are squared and regressed against the holding and the squared holding period. Performing this regression gives in the dataset the predicted values of each sales pair. In the third step, the reciprocals of the square roots of these predicted values are using as weights while performing the first step regression again. The third step regression outcomes are exponentiated, in order to get the proper indexes for both neighborhoods. Visual inspection of the constructed indexes showed that the apartment prices lag about one year to Dutch real GDP. Therefore, for the hypotheses and testing, this lag of one year is taken into account. Two-sample t significance tests are performed to test the hypotheses with regard to the apartment price indexes. Even though the used datasets for both neighborhoods have a non normal distribution, both sample sizes are large enough for performing such a test. The tests are performed unpooled tests, while the two samples (the datasets of the two neighborhoods) are quite different in size. Further, the constructed apartment price indexes are aligned to the two periods suspicious of redlining for Rotterdam (in combination with the alignment to real GDP turning points). However, only one of the suspicious periods can be investigated with the apartment price indexes while the other suspicious period is too short in length. To solve this problem, a counting table of number of transactions is made for each neighborhood. Only transactions of apartments suspicious of fraudulent acting or being of non-arms length are removed. This counting table has half yearly periods and is aligned to the two suspicious periods of redlining. For each neighborhood, next to the actual number of transactions, the total number of apartments that are present in the used dataset is divided by the number of transactions per period. This to get the turnover iii ratio for each neighborhood. Then the turnover ratio of the ‘bad’ standing neighborhood is divided by the turnover ratio of the ‘good’ standing neighborhood, to see more clearly whether and when the situation deteriorates for the ‘bad’ standing neighborhood that is suspicious of redlining. Also periods suspicious of less strict redlining and a probable catch up period are investigated. For this thesis, redlining is expected to have immediate impact on the number of transactions, which is used for the hypotheses and testing. The hypothesis regarding the counting table of number of transactions is visually tested. No statistical test can be performed, while no standard deviation or standard error is available. The outcome of the research is that apartment prices in ‘bad’ standing neighborhoods develop the same as in ‘good’ standing neighborhoods, even in the distinct phases of recession and economic upturn. The suspicious of redlining is not supported by the data used for this thesis. iv Preface During and already before my study at the Erasmus University, I am very interested in real estate and the pricing mechanism at this market. This mechanism has highly interwoven and sideway factors. For my bachelor thesis the subject was the manner how to decide whether or not to invest in certain real estate projects, and commercial and non-commercial real estate companies were compared. The work of Suchomel (2009) gave direct rise to the subject of this thesis.