  Accurate house prediction is of great significance to various real estate
stakeholders such as house owners, buyers, investors, and agents. We propose a
location-centered prediction framework that differs from existing work in terms
of data profiling and prediction model. Regarding data profiling, we define and
capture a fine-grained location profile powered by a diverse range of location
data sources, such as transportation profile (e.g., distance to nearest train
station), education profile (e.g., school zones and ranking), suburb profile
based on census data, facility profile (e.g., nearby hospitals, supermarkets).
Regarding the choice of prediction model, we observe that a variety of
approaches either consider the entire house data for modeling, or split the
entire data and model each partition independently. However, such modeling
ignores the relatedness between partitions, and for all prediction scenarios,
there may not be sufficient training samples per partition for the latter
approach. We address this problem by conducting a careful study of exploiting
the Multi-Task Learning (MTL) model. Specifically, we map the strategies for
splitting the entire house data to the ways the tasks are defined in MTL, and
each partition obtained is aligned with a task. Furthermore, we select specific
MTL-based methods with different regularization terms to capture and exploit
the relatedness between tasks. Based on real-world house transaction data
collected in Melbourne, Australia. We design extensive experimental
evaluations, and the results indicate a significant superiority of MTL-based
methods over state-of-the-art approaches. Meanwhile, we conduct an in-depth
analysis on the impact of task definitions and method selections in MTL on the
prediction performance, and demonstrate that the impact of task definitions on
prediction performance far exceeds that of method selections.
