Explaining Interest Rates in the Dutch Mortgage Market: A Time Series Analysis

Transcription

1 Explaining Interest Rates in the Dutch Mortgage Market: A Time Series Analysis by: Machiel Mulder and Mark Lengton In order to explain the development in mortgage interest rates in the Dutch market, we conducted a time-series analysis using monthly data. Because of non-stationarity of these data, we estimated the model in both first and second differences. By adding the lagged dependent variable in these models we removed the remaining serial correlation. It appears that the lagged dependent variable is the most important explanatory variable, while we do not find a significant effect of the mortgage costs, risks in the money market or market structure. We conclude that in the short term the mortgage interest rates are mainly determined by factors having a prolonged influence, while the immediate influences appear to be relatively small. Introduction Since a number of years financial markets have been stirred up and the Dutch mortgage market is no exception to this. Because of a perceived high level of the mortgage interest rates, a number of parties have complained about the functioning of the mortgage market in the Netherlands. In their view, the market for mortgages is not functioning competitively, as financing costs for banks have drastically fallen over the last two years, while the mortgage interests rates for consumers have been fairly stable (see Figure 1). In order to get a better insight into the factors driving mortgage interest rates, the NMa conducted an in-depth analysis of the mortgage market (NMa, 2011). This analysis included two types of econometric analysis: a panel analysis on annual data per bank and a time-series analysis on monthly data on industry level (see Mulder and Lengton, 2011). In this paper, we describe the time series analysis. Machiel Mulder and Mark Lengton Machiel Mulder is deputy Chief Economist of the Netherlands Competition Authority, visiting researcher at the Department of Economics and Business of the University of Groningen and extramural fellow at the Tilburg Law and Economics Centre. Mark Lengton is free-lance employee at the Office of the Chief Economist of the Netherlands Competition Authority and a MSc. Economics student at Tilburg University. Literature The pioneering study in the field of bank interest margins is Ho and Saunders (1981). These authors find that there are two major factors driving the interest margins: the degree of competition as well as the interest rate risk banks are exposed to. Maudos and Guevara (2004), using data on the banking markets in the European Union, find significant and positive effects of a number of factors, including market power, credit risk and operating costs. These results were confirmed by Hawtreyand Liang (2008). Titman and Tompaidis (2005) conclude, on the basis of individual mortgages from 1992 till 2002, that higher default risk also contributes to higher margins. Dietrich and Wunderlin (2010), however, found for the Swiss market significant negative effects for market concentration (measured by HHI). Model In a time-series analysis at industry level, we use monthly data to assess the effect of a number of variables on the average mortgage interest rate in the Dutch market. The dependent variable is the average monthly mortgage interest rate (I), while the explanatory variables refer to costs (C), risks (R) and market structure (S): I t = β 0 + β 1 C t + β 2 R t + β 3 S t + ε t (1) The mortgage costs (C) indicate the price banks have to pay for financial capital. These costs are based on Euribor rates, swap rates, interest rates on deposits, interest rates with residential mortgage backed securities (RMBS) premiums and interest rates with credit default 26 AENORM vol. 19 (73) December 2011

2 swap (CDS) premiums (see NMa, 2011). We expect that these costs are positively related to the mortgage interest rates. Although these costs also include premiums to reduce risks, they do not cover all risks which banks face. If the durations of funding contracts do not fully match the durations of the mortgage contracts, banks face a money market risk (see e.g. Maudos and Guevara, 2004; Hawtrey and Liang, 2008). 1 In addition, banks are subject to default risk which may require higher interest rates as coverage (see e.g. Titman Tompaidis, 2005). In this timeseries model at industry level, it is not possible to include the latter risk as this risk differs among banks. The money market risk can, however, be measured by the so-called normalised swaption volatility. If this volatility goes up, we expect mortgage interest rates to go up. The influence of market structure on interest rates is measured by the C3, which is the share of the three largest suppliers in the total market. High levels of C3 suggest that a small number of banks may have market power that they can leverage to increase prices and earn higher profits. We therefore expect C3 to be positively related to the mortgage interest rates (see e.g. Maudos and Guevara, 2004; Hawtrey and Liang. 2008). Data The characteristics of the data used are described in Tables 1 and 2. It appears that a fairly strong correlation exists between the mortgage interest rate on the one hand and the mortgage costs and the normalised swaption volatility on the other hand. The mortgage interest rate and the mortgage costs are depicted in Figure 1. From that figure we learn that the mortgage interest rate steadily rose from the end of 2005 until After that, the interest rate declined by approximately 1-percentage point. From the figure we also learn that the mortgage costs increased slightly from Figure 1. Mortgage interest rate and mortgage costs (8/ /2010). Source: see NMa (2011) Figure 3. Market structure of the Dutch mortgage market, measured by C3 (monthly averages, ). Source: Kadaster. Figure 2. Normalised swaption volatility, 8/ /2010. Source: Interbancaire data/super Derivatives (R). Figure 4. Profitability per type of mortgage, measured by the Lerner index (1/ /2010). 1 Imperfections in the matching of the funding might be due to pipeline risk or prepayment risk (see NMa, 2011). AENORM vol. 19 (73) December

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4 Table 1. Description of variables (8/ /2010) (n=75). Variable Mean Standard deviation Minimum Maximum Mortgage interest rate (I) Mortgage costs (C) Normalised Swaption volatility (R) C3 (S) Souces: Moneyview (I), DNB (C), Interbancaire data/super Derivatives (R), Kadaster (C3); note that both R and S are divided by 100 for scaling reasons. Table 2. Correlation matrix of variables (8/ /2010) (n=75). Variable Mortgage interest Mortgage Normalised swap- C3 rate (I) costs (C) tion volatility (S) Mortgage interest rate (I) 1.00 Mortgage costs (C) Normalised Swaption volatility (R) C3 (S) until mid 2008 before it decreased sharply in the second half of As a measure for risk in the money market we use the normalised swaption volatility. A higher volatility indicates higher uncertainty about the future cost of interbank lending. Figure 2 clearly shows that the risk strongly increased at the end of 2008, but reduced to normal levels afterwards. Figure 3, depicting the development in C3 and HHI, shows that the Dutch mortgage market became more concentrated during the years , in particular in the most recent years. On the basis of the mortgage interest rate and sourcing costs, we are able to calculate the marginal profit, which is the coverage for non-financial costs as well as for fixed costs. An indicator to measure the marginal profit is the Lerner-index, which is the difference between the mortgage interest rate (I) and the sourcing costs (C), scaled by the mortgage interest rate: (I t C t ) / I t. It appears that the marginal profits at the industry level slightly decreased from 2004 until the end of 2008 (see Figure 4). Afterwards, the profits increased strongly. This holds true for the various types of mortgages, although in some cases (such as variable interest ) this pattern is more explicit than in others (such as 10 years fixed interest ). Results In a time-series analysis on high-frequency data, nonstationarity and autocorrelation may seriously distort the analysis. Although theoretically one can doubt why interest rates would be non-stationary, in practice nonstationarity is often the case (End, 2011). 2 Testing our data on non-stationarity with the augmented Dickey- Fuller test, we indeed cannot reject the null hypothesis of non-stationarity (unit root) (see Table 3). As the residuals of an OLS-regression appear to be stationary, we can conclude that our data are cointegrated. In order to correct for non-stationarity, we estimate the model in both first and second differences. The model in first differences as well as the model in second differences show autocorrelation. This follows from the Breusch-Godfrey test (see Table 4). In order to correct for autocorrelation, we add the lagged dependent variable in these models as an explanatory variable. The Breusch-Godfrey test shows that this specification solves the autocorrelation problem in the second differences model, but not in the first differences one. Therefore we also present the Newey-West t-statistics, which correct for the impact of autocorrelation. Table 3. Augmented Dickey-Fuller test for unit root. Variable Level 1st 2nd Difference Difference Mortgage interest rate Mortgage costs Normalised swaption volatility C Note: The critical values are (1%), (5%) and (10%). 2 Interest rates do not follow a structural rising pattern, as for instance macroeconomic quantities do. In the long term, these data have a kind of an anchor, which creates stationarity. AENORM vol. 19 (73) December

5 Table 4. Results of time-series analysis in different model specifications. Mortgage interest rate Model in 1st Model in 1st Model in 2nd Model in 2nd differences differences plus lag differences differences plus lag dependent variable dependent variable Mortgage costs (1.56/1.79) (1.01/1.39) (-1.69/-1.98) (-1.77/-1.98) Normalised swaption volatility (.26/.34) (-1.19/-1.62) (-.98/-1.52) (-.40/-.60) C (-.78/-.91) (-.38/-.50) (.31/.50) (-.10/-.12) Mortgage interest rate(-1) (4.92/3.91) (-3.16/-2.48) Constant (.02/.01) (-.03/-.03) (-.05/-.07) (.01/.01) Goodness of fit adj. R 2 1% 25% 3% 15% Bgodfrey (P>chi 2 ) Note: t-statistics in parentheses (normal/newey-west) Conclusion We do not find a significant effect of mortgage costs, risks in the money market as well as market structure on the monthly mortgage interest rate. It appears that the lagged dependent variable is the only statistically significant explanatory variable. Hence, we conclude that in the short term the mortgage interest rates are mainly determined by factors that have a prolonged influence, while the immediate effects appear to be relatively small. In order to explain the mortgage interest rate, less frequent data has to be used. In a panel analysis on annual data, we were indeed able to determine the contribution of these factors to the changes in the mortgage interest rate (see Mulder and Lengton, 2011). Mulder, M. and M. Lengton. Competition and interest rates in the Dutch mortgage market: an econometric analysis over NMa, The Hague, NMa Working Paper 5, NMa. Sectorstudie Hypotheekmarkt; een onderzoek naar de concurrentieomstandigheden op de Nederlandse Hypotheekmarkt. The Hague, Titman, S., S. Tompaidis. Determinants of credit spreads in commercial mortgages. Real Estate Economics 33.4 (2005): References Dietrich, A., C. Wunderlin. What drives the margins of mortgage loans? Institute of Financial Services IFZ, Lucerne University, Switzerland, Hawtrey, K., H. Liang. Bank interest margins in OECD countries. North American Journal of Economics and Finance 19 (2008): Ho, T.S.Y. and A. Saunders. The determinants of bank interest margins: theory and empirical evidence. Journal of Financial and Quantitative Analysis 16(1981): Maudos, J., J. F. Fernández de Guevara. Factors explaining the interest margin in the banking sectors of the European Union. Journal of Banking & Finance 28 (2004): AENORM vol. 19 (73) December 2011