Housing econometrics: interpretation of a quadratic variable I'm working a location based home pricing model and I have a regression which looks like this:

$$Price=\beta_0+\beta_1x_1+\beta_2x_1^2+\beta_3x_2+\beta_4x_2^2$$

where $x_1$ is longitude and $x_2$ is latitude. 

I'm finding statistical significance in longitude, longitude squared and latitude squared but not in regular latitude itself. I know that in economic theory quadratic terms are used to capture the effect of diminishing returns, but in this case I find it odd that the quadratic is significant but the regular variable isn't.

How do I interpret these results?
When both the level and the square affect the dependent variable (and if the coefficient on the square is negative), we have a case where the variable in question initially affects positively the dependent variable, but after a point it affects it negatively. 

When only the square is deemed to affect the dependent variable, then the effect is monotonic and accelerating, while its direction is indicated by the sign of the coefficient.

Real world interpretation: can you rationalize why latitude could have a monotonic effect, while longitude has not? Can you match this to the geographical areas you use data on? Is there anything special along latitude as regards the weather, socioeconomic conditions etc? And also, why longitude has an up-and-down effect? What could be the possible reason here?  

These are the cases when empirical studies become thrilling: when unexpected econometric results provide new insights into the real world.