Accuracy Metrics

Accuracy metrics measure how well the index predicts actual property values. We will demonstrate how to calculate and visualize accuracy metrics.

Basic Setup

First, we will import the necessary modules, load some sales data, and create an index:

>>> from hpipy.datasets import load_seattle_sales
>>> from hpipy.price_index import RepeatTransactionIndex

>>> df = load_seattle_sales()

>>> hpi = RepeatTransactionIndex.create_index(
...     trans_data=df,
...     prop_id="pinx",
...     trans_id="sale_id",
...     price="sale_price",
...     date="sale_date",
...     periodicity="M",
...     estimator="robust",
...     log_dep=True,
...     smooth=True,
... )

Calculating Accuracy

To calculate the accuracy of the index, you can use the accuracy function:

>>> from hpipy.utils.metrics import accuracy
>>> from hpipy.utils.plotting import plot_index_accuracy

>>> acc = accuracy(hpi)
>>> acc.round(5).head()
   pair_id  rt_price    pred_price    error  log_error  pred_period
0        1    577200  555423.08556 -0.03773   -0.03846           75
1        2    488737  458143.88008 -0.06260   -0.06464           63
2        3    570000  582619.73327  0.02214    0.02190           77
3        4    402500  416336.17537  0.03438    0.03380           63
4        5    385000  429625.48835  0.11591    0.10967           69

>>> plot_index_accuracy(acc).properties(
...     title={"text": "Accuracy Metrics", "anchor": "middle"}
... )
alt.VConcatChart(...)

Rolling Window Analysis

You can also analyze accuracy over different time windows:

>>> rolling_acc = accuracy(hpi, window=12)
>>> rolling_acc.round(5).head()
   pair_id  rt_price    pred_price    error  log_error  pred_period
0        1    577200  555423.08556 -0.03773   -0.03846           75
1        2    488737  458143.88008 -0.06260   -0.06464           63
2        3    570000  582619.73327  0.02214    0.02190           77
3        4    402500  416336.17537  0.03438    0.03380           63
4        5    385000  429625.48835  0.11591    0.10967           69

The window parameter specifies the number of periods to use for the rolling calculation.