I know perceptron is a binary classifier which has a 0/1 output. But in one of my exercises for a Neural Network course, there is a question that asks to implement a linear regression with perceptron. Unfortunately, I have no idea.
The difference between linear regression and logistic regression is the activation function, which converts the logits (W$\cdot$X) to a probability-like value.
If you specifically need to keep the activation function in the perceptron algorithm, then I would say you have to consider it as a multi-class classification problem, where the classes are all the house prices. For example, class1 = 1000, class2 = 1001 ... classN = [max] price. This should give you the closest thing to regression. Look at this if you want Multi-class Perceptron
You can also reduce the number of class by grouping house prices. For ex., prices [1000 - 1100] become 1050. This, however, will reduce your model's accuracy.