estimate the model, we use the System Generalized Method of Moment (GMM) estimator developed by Blundell and Bond (1998). The estimator combines two sets of equations. The first set includes firstdifference equations where the right-hand side variables are instrumented by the levels of the series lagged one period or more. The second set consists of the equations in levels with the right-hand side variables being instrumented by lagged first- or higher-order differences.

This estimator has several advantages<sup>5</sup> such as taking into account country-specific effects, while enabling us to address issues associated with endogeneity, measurement errors, and omitted variables. By exploiting internal instruments, the System GMM estimator removes the often hard task of identifying valid external instruments consisting of variables that are correlated with the endogenous explanatory variable, but not with the error term of the equation.

The validity of these internal instruments (lagged variables in level and first differences) was not rejected. As suggested by Arellano and Bond (1991), and Blundell and Bond (1998), a Sargan-Hansen test of overidentifying restrictions and a serial correlation test were carried out. In both instances, the null hypothesis could not be rejected—the instrumental variables are not correlated with the residual, and the errors exhibit no second-order serial correlation.

In addition, to limit the risk of over-instrumentation, we keep the number of instruments to the minimum by using as instrument only the first valid lagged value of the right-hand side variables. We assume that financial variables are endogenous, and therefore are instrumented by their second lag value, while the other variables, treated as predetermined, are instrumented by their first lag value.

# **4.3. Results analysis and discussion**

We present below estimation results of the three models using variables.

# Housing finance determinants

Table 4.1 below presents results on housing finance determinants. It shows that stock market capitalization, the growth rate of the urban population, and the dummy variable of lagged conflict are key determinants of housing finance in SSA. Stock market capitalization and the growth rate of urban population are the strongest positive determinants, while recovery from conflict is not significant for some specifications. In addition, some variables seem to be hampering housing finance depending on the economic environment. These variables are: the credit to the private sector, which is a proxy of the development of the banking sector and trade openness, which may mask the low housing finance in oil exporting countries.

**Table 4.1:** Housing finance determinants (housing finance depth)

||**Mo**|**del A1: Housing fi**|**nance determina**|**nts(housing financ**|**e depth)**|
|---|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|
|**Market capitalization**|**0.001*****|**0.001*****|**0.001*****|**0.001*****|**0.001*****|
||**(0.00005)**|**(0.00005)**|**(0.00005)**|**(0.00006)**|**(0.00005)**|
|**Urban population growth**|**0.0001***<br>**(0.00008)**|**0.0002****<br>**(0.00008)**|**0.0001***<br>**(0.00008)**|**0.0001***<br>**(0.00006)**|**0.0002****<br>**(0.00008)**|
|Conflict dummy t-1|**0.007****|**0.008****|**0.007****|0.004|0.009|
||**(0.003)**|**(0.003)**|**(0.003)**|(0.002)|(0.003)|
|Investment stock|-0.0001|0.0005|-0.0001|0.0006|-0.002|
||(0.002)|(0.002)|(0.002)|(0.002)|(0.002)|

> 5 Bond, Hoeffler, and Temple (2001) offer a good overview on GMM estimation of empirical growth models.

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