Thanks for the reply Achim.
The reason I suspect autocorrelation is because I think that  within the
same neighborhood, homes sold a few months back are likely to impact the
price of homes sold subsequently. In fact the DW test and Breusch-Pagan
test come out to be significant. So even though the data is not time series
(that is, I do not have repeated observations for the same house),
however, the houses sold close in time to each other are in the data set.

Thanks,
Shish

On Tue, Mar 26, 2013 at 3:51 PM, Achim Zeileis <Achim.Zeileis@uibk.ac.at>wrote:

> On Tue, 26 Mar 2013, SHISHIR MATHUR wrote:
>
>  Hello:
>> My dataset set contains several thousand rows of data, with each row
>> containing information for a house. The variables include the sale price
>> of
>> the house, the quarter and year of sale, the attributes of the house, and
>> the attributes of the neighborhood and the city in which the house is
>> located. The data is for a 10-year period. No house is repeated in the
>> dataset. In summary, the dataset can be termed pooled cross-section data.
>>
>> My question: Can I estimate Newey-West HAC standard errors for a model
>> that
>> estimates the effect of various independent variables on the sale price of
>> the house?  My understanding is that Newey-West can be used for time
>> series
>> and panel data. However, I am not sure whether it can be used for pooled
>> cross-section data.  If yes, can you refer me to a specific source, such
>> as
>> a paper or a book?
>>
>
> The result of your aggregation is a cross-section data set. Thus, there
> should be no correlation between the different observations - or in other
> terms, the ordering of your observations is completely arbitrary.
>
> Consequently, there may be heteroskedasticity but not autocorrelation. So
> you may use HC standard errors but HAC should not be necessary. (Using HAC
> standard errors will still be consistent but less efficient.)
>
>
>  --
>> Best,
>> Shish
>>
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>>
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>>


-- 
Best,
Shishir

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