openavmkit.utilities.modeling

AverageModel

AverageModel(type, sales_chase)

An intentionally bad predictive model, to use as a sort of control. Produces predictions equal to the average of observed sale prices.

Attributes:

Name	Type	Description
type	str	The type of average to use
sales_chase	float	Simulates sales chasing. If 0.0, no sales chasing will occur. For any other value, predictions against sold parcels will chase (copy) the observed sale price, with a bit of random noise equal to the value of sales_chase. So sales_chase=0.05 will copy each sale price with 5% random noise. NOTE: This is for analytical purposes only, one should not intentionally chase sales when working in actual production.

Initialize an AverageModel

Parameters:

Name	Type	Description	Default
type	str	The type of average to use	required
sales_chase	float	Simulates sales chasing. If 0.0, no sales chasing will occur. For any other value, predictions against sold parcels will chase (copy) the observed sale price, with a bit of random noise equal to the value of sales_chase. So sales_chase=0.05 will copy each sale price with 5% random noise. NOTE: This is for analytical purposes only, one should not intentionally chase sales when working in actual production.	required

Source code in openavmkit/utilities/modeling.py

def __init__(self, type: str, sales_chase: float):
    """Initialize an AverageModel

    Parameters
    ----------
    type : str
        The type of average to use
    sales_chase : float
        Simulates sales chasing. If 0.0, no sales chasing will occur. For any other value, predictions against sold
        parcels will chase (copy) the observed sale price, with a bit of random noise equal to the value of
        ``sales_chase``. So ``sales_chase=0.05`` will copy each sale price with 5% random noise.
        **NOTE**: This is for analytical purposes only, one should not intentionally chase sales when working in actual production.
    """
    self.type = type
    self.sales_chase = sales_chase

GWRModel

GWRModel(coords_train, X_train, y_train, gwr_bw)

Geographic Weighted Regression Model

Attributes:

Name	Type	Description
coords_train	list[tuple[float, float]]	list of geospatial coordinates corresponding to each observation in the training set
X_train	ndarray	2D array of independent variables' values from the training set
y_train	ndarray	1D array of dependent variable's values from the training set
gwr_bw	float	Bandwidth for GWR calculation

Parameters:

Name	Type	Description	Default
coords_train	list[tuple[float, float]]	list of geospatial coordinates corresponding to each observation in the training set	required
X_train	ndarray	2D array of independent variables' values from the training set	required
y_train	ndarray	1D array of dependent variable's values from the training set	required
gwr_bw	float	Bandwidth for GWR calculation	required

Source code in openavmkit/utilities/modeling.py

def __init__(
    self,
    coords_train: list[tuple[float, float]],
    X_train: np.ndarray,
    y_train: np.ndarray,
    gwr_bw: float,
):
    """
    Parameters
    ----------
    coords_train : list[tuple[float, float]]
        list of geospatial coordinates corresponding to each observation in the training set
    X_train : np.ndarray
        2D array of independent variables' values from the training set
    y_train : np.ndarray
        1D array of dependent variable's values from the training set
    gwr_bw : float
        Bandwidth for GWR calculation
    """
    self.coords_train = coords_train
    self.X_train = X_train
    self.y_train = y_train
    self.gwr_bw = gwr_bw

GarbageModel

GarbageModel(min_value, max_value, sales_chase, normal)

An intentionally bad predictive model, to use as a sort of control. Produces random predictions.

Attributes:

Name	Type	Description
min_value	float	The minimum value of to "predict"
max_value	float	The maximum value of to "predict"
sales_chase	float	Simulates sales chasing. If 0.0, no sales chasing will occur. For any other value, predictions against sold parcels will chase (copy) the observed sale price, with a bit of random noise equal to the value of sales_chase. So sales_chase=0.05 will copy each sale price with 5% random noise. NOTE: This is for analytical purposes only, one should not intentionally chase sales when working in actual production.
normal	bool	If True, the randomly generated predictions follow a normal distribution based on the observed sale price's standard deviation. If False, randomly generated predictions follow a uniform distribution between min and max.

Initialize a GarbageModel

Parameters:

Name	Type	Description	Default
min_value	float	The minimum value of to "predict"	required
max_value	float	The maximum value of to "predict"	required
sales_chase	float	Simulates sales chasing. If 0.0, no sales chasing will occur. For any other value, predictions against sold parcels will chase (copy) the observed sale price, with a bit of random noise equal to the value of sales_chase. So sales_chase=0.05 will copy each sale price with 5% random noise. NOTE: This is for analytical purposes only, one should not intentionally chase sales when working in actual production.	required
normal	bool	If True, the randomly generated predictions follow a normal distribution based on the observed sale price's standard deviation. If False, randomly generated predictions follow a uniform distribution between min and max.	required

Source code in openavmkit/utilities/modeling.py

def __init__(
    self, min_value: float, max_value: float, sales_chase: float, normal: bool
):
    """Initialize a GarbageModel

    Parameters
    ----------
    min_value : float
        The minimum value of to "predict"
    max_value : float
        The maximum value of to "predict"
    sales_chase : float
        Simulates sales chasing. If 0.0, no sales chasing will occur. For any other value, predictions against sold
        parcels will chase (copy) the observed sale price, with a bit of random noise equal to the value of
        ``sales_chase``. So ``sales_chase=0.05`` will copy each sale price with 5% random noise.
        **NOTE**: This is for analytical purposes only, one should not intentionally chase sales when working in actual production.
    normal : bool
        If True, the randomly generated predictions follow a normal distribution based on the observed sale price's
        standard deviation. If False, randomly generated predictions follow a uniform distribution between min and max.
    """
    self.min_value = min_value
    self.max_value = max_value
    self.sales_chase = sales_chase
    self.normal = normal

GroundTruthModel

GroundTruthModel(observed_field, ground_truth_field)

Mostly only used in Synthetic models, where you want to compare against simulation ground_truth instead of observed sale price, which you can never do in real life.

Attributes:

Name	Type	Description
observed_field	str	The field that represents observed sale prices
ground_truth_field	str	The field that represents platonic ground truth

Initialize a GroundTruthModel object

Parameters:

Name	Type	Description	Default
observed_field	str	The field that represents observed sale prices	required
ground_truth_field	str	The field that represents platonic ground truth	required

Source code in openavmkit/utilities/modeling.py

def __init__(self, observed_field: str, ground_truth_field: str):
    """Initialize a GroundTruthModel object

    Parameters
    ----------
    observed_field : str
        The field that represents observed sale prices
    ground_truth_field : str
        The field that represents platonic ground truth
    """
    self.observed_field = observed_field
    self.ground_truth_field = ground_truth_field

LocalSqftModel

LocalSqftModel(loc_map, location_fields, overall_per_impr_sqft, overall_per_land_sqft, sales_chase)

Produces predictions equal to the localized average price/sqft of land or building, multiplied by the observed size of the parcel's land or building, depending on whether it's vacant or improved.

Unlike NaiveSqftModel, this model is sensitive to location, based on user-specified locations, and might actually result in decent predictions.

Attributes:

Name	Type	Description
loc_map	dict[str : tuple[DataFrame, DataFrame]	A dictionary that maps location field names to localized per-sqft values. The dictionary itself is keyed by the names of the location fields themselves (e.g. "neighborhood", "market_region", "census_tract", etc.) or whatever the user specifies. Each entry is a tuple containing two DataFrames: Values per improved square foot Values per land square foot Each DataFrame is keyed by the unique values for the given location. (e.g. "River heights", "Meadowbrook", etc., if the location field in question is "neighborhood") The other field in each DataFrame will be {location_field}_per_impr_sqft or {location_field}_per_land_sqft
location_fields	list	List of location fields used (e.g. "neighborhood", "market_region", "census_tract", etc.)
overall_per_impr_sqft	float	Fallback value per improved square foot, to use for parcels of unspecified location. Based on the overall average value for the dataset.
overall_per_land_sqft	float	Fallback value per land square foot, to use for parcels of unspecified location. Based on the overall average value for the dataset.
sales_chase	float	Simulates sales chasing. If 0.0, no sales chasing will occur. For any other value, predictions against sold parcels will chase (copy) the observed sale price, with a bit of random noise equal to the value of sales_chase. So sales_chase=0.05 will copy each sale price with 5% random noise. NOTE: This is for analytical purposes only, one should not intentionally chase sales when working in actual production.

Initialize a LocalSqftModel

Parameters:

Name	Type	Description	Default
loc_map	dict[str : tuple[DataFrame, DataFrame]	A dictionary that maps location field names to localized per-sqft values. The dictionary itself is keyed by the names of the location fields themselves (e.g. "neighborhood", "market_region", "census_tract", etc.) or whatever the user specifies. Each entry is a tuple containing two DataFrames: Values per improved square foot Values per land square foot Each DataFrame is keyed by the unique values for the given location. (e.g. "River heights", "Meadowbrook", etc., if the location field in question is "neighborhood") The other field in each DataFrame will be {location_field}_per_impr_sqft or {location_field}_per_land_sqft	required
location_fields	list	List of location fields used (e.g. "neighborhood", "market_region", "census_tract", etc.)	required
overall_per_impr_sqft	float	Fallback value per improved square foot, to use for parcels of unspecified location. Based on the overall average value for the dataset.	required
overall_per_land_sqft	float	Fallback value per land square foot, to use for parcels of unspecified location. Based on the overall average value for the dataset.	required
sales_chase	float	Simulates sales chasing. If 0.0, no sales chasing will occur. For any other value, predictions against sold parcels will chase (copy) the observed sale price, with a bit of random noise equal to the value of sales_chase. So sales_chase=0.05 will copy each sale price with 5% random noise. NOTE: This is for analytical purposes only, one should not intentionally chase sales when working in actual production.	required

Source code in openavmkit/utilities/modeling.py

def __init__(
    self,
    loc_map: dict,
    location_fields: list,
    overall_per_impr_sqft: float,
    overall_per_land_sqft: float,
    sales_chase: float,
):
    """Initialize a LocalSqftModel

    Parameters
    ----------
    loc_map : dict[str : tuple[DataFrame, DataFrame]
        A dictionary that maps location field names to localized per-sqft values. The dictionary itself is keyed by the
        names of the location fields themselves (e.g. "neighborhood", "market_region", "census_tract", etc.) or whatever
        the user specifies.

        Each entry is a tuple containing two DataFrames:

          - Values per improved square foot
          - Values per land square foot

        Each DataFrame is keyed by the unique *values* for the given location. (e.g. "River heights", "Meadowbrook",
        etc., if the location field in question is "neighborhood") The other field in each DataFrame will be
        ``{location_field}_per_impr_sqft`` or ``{location_field}_per_land_sqft``
    location_fields : list
        List of location fields used (e.g. "neighborhood", "market_region", "census_tract", etc.)
    overall_per_impr_sqft : float
        Fallback value per improved square foot, to use for parcels of unspecified location. Based on the
        overall average value for the dataset.
    overall_per_land_sqft : float
        Fallback value per land square foot, to use for parcels of unspecified location. Based on the overall average
        value for the dataset.
    sales_chase : float
        Simulates sales chasing. If 0.0, no sales chasing will occur. For any other value, predictions against sold
        parcels will chase (copy) the observed sale price, with a bit of random noise equal to the value of
        ``sales_chase``. So ``sales_chase=0.05`` will copy each sale price with 5% random noise.
        **NOTE**: This is for analytical purposes only, one should not intentionally chase sales when working in actual production.
    """
    self.loc_map = loc_map
    self.location_fields = location_fields
    self.overall_per_impr_sqft = overall_per_impr_sqft
    self.overall_per_land_sqft = overall_per_land_sqft
    self.sales_chase = sales_chase

MRAModel

MRAModel(fitted_model, intercept)

Multiple Regression Analysis Model

Plain 'ol (multiple) linear regression

Attributes:

Name	Type	Description
fitted_model	RegressionResults	Fitted model from running the regression
intercept	bool	Whether the model was fit with an intercept or not.

Source code in openavmkit/utilities/modeling.py

def __init__(self, fitted_model: RegressionResults, intercept: bool):
    self.fitted_model = fitted_model
    self.intercept = intercept

NaiveSqftModel

NaiveSqftModel(dep_per_built_sqft, dep_per_land_sqft, sales_chase)

An intentionally bad predictive model, to use as a sort of control. Produces predictions equal to the prevailing average price/sqft of land or building, multiplied by the observed size of the parcel's land or building, depending on whether it's vacant or improved.

Attributes:

Name	Type	Description
dep_per_built_sqft	float	Dependent variable value divided by improved square footage
dep_per_land_sqft	float	Dependent variable value divided by land square footage
sales_chase	float	Simulates sales chasing. If 0.0, no sales chasing will occur. For any other value, predictions against sold parcels will chase (copy) the observed sale price, with a bit of random noise equal to the value of sales_chase. So sales_chase=0.05 will copy each sale price with 5% random noise. NOTE: This is for analytical purposes only, one should not intentionally chase sales when working in actual production.

Initialize a NaiveSqftModel

Parameters:

Name	Type	Description	Default
dep_per_built_sqft	float	Dependent variable value divided by improved square footage	required
dep_per_land_sqft	float	Dependent variable value divided by land square footage	required
sales_chase	float	Simulates sales chasing. If 0.0, no sales chasing will occur. For any other value, predictions against sold parcels will chase (copy) the observed sale price, with a bit of random noise equal to the value of sales_chase. So sales_chase=0.05 will copy each sale price with 5% random noise. NOTE: This is for analytical purposes only, one should not intentionally chase sales when working in actual production.	required

Source code in openavmkit/utilities/modeling.py

def __init__(
    self, dep_per_built_sqft: float, dep_per_land_sqft: float, sales_chase: float
):
    """Initialize a NaiveSqftModel

    Parameters
    ----------
    dep_per_built_sqft: float
        Dependent variable value divided by improved square footage
    dep_per_land_sqft: float
        Dependent variable value divided by land square footage
    sales_chase : float
        Simulates sales chasing. If 0.0, no sales chasing will occur. For any other value, predictions against sold
        parcels will chase (copy) the observed sale price, with a bit of random noise equal to the value of
        ``sales_chase``. So ``sales_chase=0.05`` will copy each sale price with 5% random noise.
        **NOTE**: This is for analytical purposes only, one should not intentionally chase sales when working in actual production.
    """
    self.dep_per_built_sqft = dep_per_built_sqft
    self.dep_per_land_sqft = dep_per_land_sqft
    self.sales_chase = sales_chase

PassThroughModel

PassThroughModel(field)

Mostly used for representing existing valuations to compare against, such as the Assessor's values

Attributes:

Name	Type	Description
field	str	The field that holds the values you want to pass through as predictions

Initialize a PassThroughModel

Parameters:

Name	Type	Description	Default
field	str	The field that holds the values you want to pass through as predictions	required

Source code in openavmkit/utilities/modeling.py

def __init__(
    self,
    field: str,
):
    """Initialize a PassThroughModel

    Parameters
    ----------
    field : str
        The field that holds the values you want to pass through as predictions
    """
    self.field = field

SpatialLagModel

SpatialLagModel(per_sqft)

Use a spatial lag field as your prediction

Attributes:

Name	Type	Description
per_sqft	bool	If True, normalize by square foot. If False, use the direct value of the spatial lag field.

Initialize a SpatialLagModel

Parameters:

Name	Type	Description	Default
per_sqft	bool	If True, normalize by square foot. If False, use the direct value of the spatial lag field.	required

Source code in openavmkit/utilities/modeling.py

def __init__(self, per_sqft: bool):
    """Initialize a SpatialLagModel

    Parameters
    ----------
    per_sqft : bool
        If True, normalize by square foot. If False, use the direct value of the spatial lag field.
    """
    self.per_sqft = per_sqft