System and method for property data management

1 . A method comprising:

determining raw property data for a property, the raw property data comprising imagery of the property;

based on the raw property data, estimating, for each of a set of property attributes, an attribute value and a confidence metric using a pretrained neural network;

retrieving a set of third-party values for each property attribute of the set;

determining a respective confidence metric for each third-party value;

for each property attribute of the set of property attributes, determining a combined confidence metric based on a combination of the respective confidence metrics of the attribute value and third-party value;

automatically determining a final value for each property attribute based on the set of third-party values, the estimated attribute value, and the combined confidence metric;

automatically prefilling the final value for each property attribute of the set into a property form; and

providing the property form, prefilled with the final value for each property attribute of the set, to a remote endpoint.

2 . The method of claim 1 , further comprising: estimating the final value of at least one property attribute of the set based further on a set of neighboring properties, using a geospatial clustering algorithm.

3 . The method of claim 1 , wherein the set of property attributes comprises at least one of a roof area or a square footage of living space.

4 . The method of claim 1 , wherein the property form comprises a property valuation form.

5 . The method of claim 1 , wherein the third-party values are retrieved from at least a first source and a second source, wherein the method further comprises:

identifying an assumption of dependency between the first and second sources; and

removing duplicate third-party values for at least one property attribute based on an assumption of dependency between the first and second sources.

6 . The method of claim 1 , wherein the third-party values are retrieved from at least a first source comprising a tax assessment associated with a geographic region, wherein the respective confidence metrics are determined based on a set of predetermined biases for the geographic region.

7 . The method of claim 1 , wherein each combined confidence metric is a probability.

8 . The method of claim 1 , wherein the attribute value and the confidence metric are estimated as part of a distribution.

9 . The method of claim 1 , wherein the set of property attributes comprises a roof area and a square footage of living space.

10 . A method for property replacement cost estimation, comprising:

receiving a set of unstructured property data for a property;

with a set of pretrained machine learning (ML) models, determining an ML-model estimate based on the unstructured property data, the ML-model estimate comprising an attribute value and a confidence metric for each of a plurality of property attributes;

retrieving a set of tax assessment values, each corresponding to a respective property attribute of the plurality, wherein the tax assessment values are retrieved from a structured, third-party dataset associated with a geographic region and is structured based on rules of the geographic region;

based on a predetermined bias of the geographic region, determining a respective confidence metric for the tax assessment value;

determining a combined confidence metric based on the confidence metric associated with the estimated value and the respective confidence metric for the tax assessment value;

with a tree-based model, automatically determining a replacement cost for the property based on the set of tax assessment values, the ML model estimate, and the combined confidence metric; and

providing the replacement cost to a remote endpoint.

11 . The method of claim 10 , wherein each of the plurality of property attributes is determined with a respective pretrained ML model of the set which is pretrained for the property attribute.

12 . The method of claim 11 , wherein the respective pretrained ML model for each property attribute is pretrained using supervised learning with a training dataset which is independent of the unstructured property data and tax assessment values.

13 . The method of claim 10 , further comprising:

using the tree-based model, determining a final value for each property attribute of the plurality based on the set of tax assessment values, the ML model estimate, the confidence metric, according to a set of predetermined rules; and

automatically prefilling a property form with the final value for each property attribute; and

updating at least one final value within the property form based on a user feedback provision.

14 . The method of claim 13 , comprising: automatically determining the replacement cost based on the property form.

15 . The method of claim 10 , wherein the unstructured property data comprises: imagery and sketch data.

16 . The method of claim 15 , wherein the imagery comprises geospatial imagery and interior imagery.

17 . The method of claim 10 , wherein the attribute value and the confidence metric are estimated as a quantile distribution.