Refining a linear regression model for condominium prices I'm hoping someone here is able to help me refine a linear regression model I'm working on at work. I am in no way a statistician, but I guess I have the most experience (basic stats course and decently capable with excel) in my office.
I've been tasked with creating a model that would help predict condo prices (dependent variable) in a particular city. I've collected data from the Multiple Listing Service for use as my independent variables. The data I am collecting is from condos that have sold or are currently active within the last 6 months, and that are between 0 and 2 years old. The data is also limited to 4 storey wood-frame construction within a particular city.
The independent variables I have used are: Square footage, top floor (dummy variable), corner unit (dummy), unit type (1 bed, 2 bed etc.), exposure (dummy, direction it faces),  material spec (quality of finishings). I have since dropped exposure from the equation because it wasn't statistically significant (t stat was was around .3 - .4). All of the other coefficients have a t Stat over 2, however two of them are confusing me. The top floor and corner unit coefficients have a negative relationship when logically they should have a positive one. In my experience, top floor and corner units hold a premium over lower level and inside units. 
Does anyone have any idea why this could be? I have around 40 samples so far, would expanding my data set to include more samples help fix this? Also, I understand real estate prices can be a tricky thing to model because of subjective variables that can't really be accounted for. Anyways, any help would be appreciated as I am trying to learn about regression as I work on this!
Sincerely,
Rob
 A: Since you are looking for predictive value, you should not necessarily drop out a variable (exposure) based on a significance test. There are methods out there that select variables based on criteria more aimed at good prediction (generally based on crossvalidation or other bootstrap-alike techniques). I doubt you will find these in Excel though. I greatly advise LASSO, e.g. with any measure of predictive value (feel free to ask more info). Note that most of these techniques are basically forms of linear regression with a twitch that finds the coefficients that can be set to zero.
Your number of observations is not exactly high for your number of covariates, but if this becomes an option, it will be interesting to add interaction terms (which I understand you have not done yet).
As for reasons why this or that variable is in your model: I'd be wary of making strong statements about that from your sample size (especially considering the number of covariates, again).
A: How many top floor units are there? How many corner units? It's possible these variables are being thrown off by a couple of outliers, which isn't hard when you have so few samples. 
One thing you can do is look for dependencies between variables. Maybe all/most of the top floor units in your dataset happened to be from cheaper quality buildings, or smaller units. You won't see a dependence if the units are cheaper for a reason that isn't reflected in your independent variables, like location (probably one of the most predictive variables in housing price models). 
A: I didn't see condo fee as one of your independent variables. "Penthouse" units usually have a higher condo fee. The higher the condo fee the less the unit will sell for.
A: You say that your sample size is 40 and that:

The data I am collecting is from condos that have sold or are
  currently active within the last 6 months, and that are between 0 and
  2 years old. The data is also limited to 4 storey wood-frame
  construction within a particular city.

If I were you, I would want a larger sample.  It may be that the only way to get a larger sample is to remove some of the constraints you list above on the type of data you start with.  Instead of restricting your data to only the most recent, you could include date of sale as a predictor.  This would capture a linear trend in prices.  There are probably seasonal effects as well so you may want to include dummy/indicator variables for that as well.  You could do something similar for age-of-unit, but expect that to be non-linear - I might break it into categories.  Also, you don't mention a variable for those all important real-estate considerations: location, location or location.
