geo spatial clustering based on another feature I have data about houses for sale, that I present over a map. 

Each house has coordinates ([lat,lng]) and other features. The data is only for one country, so no need to address the 180deg world wrap.  

I want to cluster neighbourhoods, out of those houses, and base the the neighbourhood borders on prices (sqm price). 

It's really easy to see those clusters when I paint the houses with gradient color - from cheap to expensive, but I can't find a trivial way to do it mathematically.

Basically, what I want is price clustering, but with a limitation on the clustering algorithm so there won't be any geo-location overlap. 

Another way to look at it - I want to blur the data points and create low-resolution areas.

Not sure where to go with this, any help will be appreciated.
There are many such approaches, for example spatial autocorrelation, Lisa, etc.

In the clustering domain, GDBSCAN is a generalization of DBSCAN where you could easily define neighbors as points being within a certain distance and having a similar price.

Nevertheless it is probably a good idea to look into actual geostatistics and beyond the limitations of sklearn.