Radio Frequency Fingerprinting (RFF) research typically uses expensive, laboratory grade receivers which have high dynamic range, very stable oscillators, large instantaneous bandwidth, multi-rate sampling, etc. In this study, the RFF effectiveness of lower grade receivers is considered. Using software-defined radios (SDRs) of different cost and performance, a linear regression model is developed to predict RFF performance. Unlike two previous studies of SDR effectiveness that used commercial and lab-grade SDRs, the experiment here focused on hobbyist and commercial-grade SDRs (RTL-SDR, B200-mini, N210). A regression model is proposed for a generic SDR. Using a full-factorial experiment matrix, the gain, sample rate, and signal-to-noise ratio (SNR) were selected as the common control factors. The transmit sources were three commercially-available, general purpose, wireless transmitters of the same model. An SDR performance index (SPI) was developed from the percent correct classification using the Random Forest classifier for each SDR and for a generic SDR. The RFF results show that the lower-cost SDRs record the data with enough fidelity to achieve over 90% classification accuracy.