House price prediction is a popular research topic based on machine learning and deep learning models. Deep learning models have been utilized in different real-time applications to improve business capabilities. In essence, a Deep learning-based Housing price prediction model assists developers for economic growth and customers for future decision making. The existing Deep learning-based house price prediction model considers house transactions data alone for prediction, and it becomes inadequate for achieving high prediction performance.
   Thus, heterogeneous data, including street-view or satellite maps for price prediction, improves data diversity and prediction performance. The deep ensemble-based house prediction model considers heterogeneous data and significantly improves data diversity and prediction performance rather than the single deep learning model. A joint self-attention mechanism identifies crucial features that prospective house buyers consider to learn the complicated relationship between features to increase prediction precision. Hence, a Deep ensemble learning-based Housing price prediction approach with a self-attention mechanism incorporates complete knowledge of heterogeneous features and identifies the implicit relationships between different attributes, and also it improves model training performance.