Untitled

    def get_sov_valutaion(self, obj_in: dict) -> ValuatedPropertySovInputDTO:
      """
       Logic based off of this Excel file:
       https://britgroupservices.sharepoint.com/:x:/t/PricingAssessment/EWpFk6gQRgFDsi0Rgv389LwBSQelRij3oHB6m5lCQf5Udw?e=ixfnXD
       Inquire with Waqaad:
       - GrossArea is the same as floor_area
       - BuildingValue is tiv_building
      """
      obj_out = ValuatedPropertySovInputDTO(**obj_in)

      #  finding matching OccupancyQuartile match:
      filters = {"state_id": obj_in["state_id"], "construction_type_id": obj_in["construction_type_id"], "occupancy_type_id": obj_in["occupancy_type_id"]}
      quartile_match = self.db.session.query(OccupancyQuartile).filter_by(**filters).first
      if not quartile_match:
          quartile_match = self.db.session.query(OccupancyQuartile).filter(and_(OccupancyQuartile.occupancy_type_id==obj_in["occupancy_type_id"], OccupancyQuartile.construction_type_id==None, OccupancyQuartile.state_id==None)).first

      #  calculating tiv_per_sqft_quartile:
      if quartile_match:
        if obj_out.tiv_per_sqft >= quartile_match.q3:
            obj_out.tiv_per_sqft_quartile = "High"
        elif obj_out.tiv_per_sqft >= quartile_match.q1:
            obj_out.tiv_per_sqft_quartile = "Average"
        else:
            obj_out.tiv_per_sqft_quartile = "Low"

      #  calculating suggested_tiv_per_sqft:
      if quartile_match:
          obj_out.suggested_tiv_per_sqft = quartile_match.q2

      #  calculating tiv_per_sqft & suggested_tiv:
      if obj_in["floor_area"] and obj_in["floor_area"] > 1:
          obj_out.tiv_per_sqft = obj_in["tiv_building"] / obj_in["floor_area"]
          obj_out.suggested_tiv = obj_in["floor_area"] * obj_out.suggested_tiv_per_sqft
      else:
          obj_out.tiv_per_sqft = None
          obj_out.suggested_tiv = obj_in["tiv_building"]

      return obj_out