diff --git a/urbansim/models/__init__.py b/urbansim/models/__init__.py
new file mode 100644
index 00000000..1467c86f
--- /dev/null
+++ b/urbansim/models/__init__.py
@@ -0,0 +1 @@
+from .hedonic import HedonicModel
diff --git a/urbansim/models/hedonic.py b/urbansim/models/hedonic.py
new file mode 100644
index 00000000..fc945db4
--- /dev/null
+++ b/urbansim/models/hedonic.py
@@ -0,0 +1,159 @@
+import numpy as np
+import pandas as pd
+import statsmodels.formula.api as smf
+
+
+def apply_filter_query(df, filters):
+    """
+    Use the DataFrame.query method to filter a table down to the
+    desired rows.
+
+    Parameters
+    ----------
+    df : pandas.DataFrame
+    filters : list of str
+        List of filters to apply. Will be joined together with
+        ' and ' and passed to DataFrame.query.
+
+    Returns
+    -------
+    filtered_df : pandas.DataFrame
+
+    """
+    query = ' and '.join(filters)
+    return df.query(query)
+
+
+def fit_model(df, filters, model_expression):
+    """
+    Use statsmodels to construct a model relation.
+
+    Parameters
+    ----------
+    df : pandas.DataFrame
+        Data to use for fit. Should contain all the columns
+        referenced in the `model_expression`.
+    filters : list of str
+        Any filters to apply before doing the model fit.
+    model_expression : str
+        A patsy model expression that can be used with statsmodels.
+        Should contain both the left- and right-hand sides.
+
+    Returns
+    -------
+    fit : statsmodels.regression.linear_model.OLSResults
+
+    """
+    df = apply_filter_query(df, filters)
+    model = smf.ols(formula=model_expression, data=df)
+    return model.fit()
+
+
+def predict(df, filters, model_fit, ytransform=None):
+    """
+    Apply model to new data to predict new dependent values.
+
+    Parameters
+    ----------
+    df : pandas.DataFrame
+    filters : list of str
+        Any filters to apply before doing prediction.
+    model_fit : statsmodels.regression.linear_model.OLSResults
+        Result of model estimation.
+    ytransform : callable, optional
+        A function to call on the array of predicted output.
+        For example, if the model relation is predicting the log
+        of price, you might pass ``ytransform=np.exp`` so that
+        the results reflect actual price.
+
+        By default no transformation is applied.
+
+    Returns
+    -------
+    result : pandas.Series
+        Predicted values as a pandas Series. Will have the index of `df`
+        after applying filters.
+
+    """
+    df = apply_filter_query(df, filters)
+    sim_data = model_fit.predict(df)
+    if ytransform:
+        sim_data = ytransform(sim_data)
+    return pd.Series(sim_data, index=df.index)
+
+
+class HedonicModel(object):
+    """
+    A hedonic (regression) model with the ability to store an
+    estimated model and predict new data based on the model.
+
+    Parameters
+    ----------
+    fit_filters : list of str
+        Filters applied before fitting the model.
+    predict_filters : list of str
+        Filters applied before calculating new data points.
+    model_expression : str
+        A patsy model expression that can be used with statsmodels.
+        Should contain both the left- and right-hand sides.
+    ytransform : callable, optional
+        A function to call on the array of predicted output.
+        For example, if the model relation is predicting the log
+        of price, you might pass ``ytransform=np.exp`` so that
+        the results reflect actual price.
+
+        By default no transformation is applied.
+    name : str, optional
+        Optional descriptive name for this model that may be used
+        in output.
+
+    """
+    def __init__(self, fit_filters, predict_filters, model_expression,
+                 ytransform=None, name=None):
+        self.fit_filters = fit_filters
+        self.predict_filters = predict_filters
+        self.model_expression = model_expression
+        self.ytransform = ytransform
+        self.name = name or 'HedonicModel'
+        self.model_fit = None
+
+    def fit_model(self, data):
+        """
+        Fit the model to data and store/return the results.
+
+        Parameters
+        ----------
+        data : pandas.DataFrame
+            Data to use for fitting the model. Must contain all the
+            columns referenced by the `model_expression`.
+
+        Returns
+        -------
+        fit : statsmodels.regression.linear_model.OLSResults
+            This is returned for inspection, but also stored on the
+            class instance for use during prediction.
+
+        """
+        fit = fit_model(data, self.fit_filters, self.model_expression)
+        self.model_fit = fit
+        return fit
+
+    def predict(self, data):
+        """
+        Predict a new data set based on an estimated model.
+
+        Parameters
+        ----------
+        data : pandas.DataFrame
+            Data to use for prediction. Must contain all the columns
+            referenced by the right-hand side of the `model_expression`.
+
+        Returns
+        -------
+        result : pandas.Series
+            Predicted values as a pandas Series. Will have the index of `data`
+            after applying filters.
+
+        """
+        return predict(
+            data, self.predict_filters, self.model_fit, self.ytransform)