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!