The popularity, cost-effectiveness and ease of buying and selling that marketplaces like Craigslist, Offerup offer to users has been plagued with the rising number of unsolicited spam listings, fraudulent transactions and in some extreme cases law enforcement also needs to be involved. Driven by the need to protect Offerup users from this growing menace, research in spam, fraud listing filtering/detection systems has been increasingly active in the last decade. However, the adaptive nature of Scammers and Fraudsters has often rendered most of these systems ineffective. While several spam detection models have been reported in literature, the reported performance on an out of sample test data shows the room for more improvement. Presented in this research is an improved spam detection model based on Locality Sensitive Hashing algorithm which to the best of our knowledge has received little attention in spam/fraud detection problems. Experimental results show that the proposed model outperforms earlier approaches across a wide range of evaluation metrics inside Offerup.