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Rental Property Management Tips You Need In 2023
So nowadays, you need to know that the rental market is back in action. And the business of rental property management is returning to its previous situation. So this simply means that handling your property needs more than one skill. Read on to know more.
#property#property management#rentalproperty#propertymanagementtips#tenant#monthlyrent#moving#tenant security
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To ensure a hassle-free and legally sound rental experience, we offer professional renting services to assist you in creating a rental lease agreement. We will walk you through the complexities of lease terms, monthly rent details, security deposits, and property laws. Learn more about the significance of a lease agreement while renting a home by visiting our blog at https://yojinvest.com/importance-of-lease-agreement-when-renting-a-property/.
#LeaseAgreement#RentalServices#PropertyLeasing#LegalGuidance#RentingTips#LeaseTerms#MonthlyRent#SecurityDeposits#HassleFreeRenting#PropertyRules#ExpertServices#TenantRights#LandlordTips#RentalExperience#LeasingProcess#RealEstateGuidance#YojInvest#YojRental#RentalCompany#RealEstateServices
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Here's one method of determining how much you should be on monthly rent
www.1stChoiceBHL.com
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Discover affordable motels in San Diego with convenient weekly and monthly rates under $200. Find budget-friendly lodging options for extended stays, at https://www.truetravelplanner.com/cheap-motels-in-san-diego-2/
#sandiego#motelsinsandiego#sandiegomotels#200weeklymotelssandiego#motelsforweekunder200#monthlyrates
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What do you think about this? Is this a Yay or Nay for you? What do you think the Pros & Cons will be? Let’s hear your opinion 😊 #monthlyrent #realestate #realestatelagos #lagosrealestate #realestateconsulting #realestatebroker #sanwoolu #𝕋h𝕖N𝕠1ℝe𝕒l𝔼s𝕥a𝕥e𝔻o𝕔t𝕠r (at Lafiaji Beach) https://www.instagram.com/p/CF78svMF3qs/?igshid=1lsui7j12mwam
#monthlyrent#realestate#realestatelagos#lagosrealestate#realestateconsulting#realestatebroker#sanwoolu#𝕋h𝕖n𝕠1ℝe𝕒l𝔼s𝕥a𝕥e𝔻o𝕔t𝕠r
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If you haven’t already hired a professional photographer to shoot your property, you are really missing out on something GREAT.
Do You Know?
1. Buyers spend 60% of their time looking at listing photos
2. Homes with high-quality photos receive 47% higher asking price
3. Professionally photographed properties can sell for up to $19,000 more
6. Listings with professional photographs sell 32% faster.
If you are keen to know more about it fix One-to-One with an expert consultant of WOW Shoots.
#photoshoot #realestatephotos #propertyphotos #UAEproperty #dubaiproperty #property #investment #buy #sell #commercial #preleased #returns #monthlyrent #rentalproperty
#commercialpropertyforlease #propertyforsale #investmentproperty #realestateagent #realestate #commercialrealestate #commercialpropretyforsale #bestpropertyagents #realestategurugram #listings #realestatenews #propertysales #realestateinvestor #realestate
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https://www.theprelease.com/property/himalaya-optics/
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Cool Filipino Punk 'zine & 7" comp with Lapu-Lapu killing Magellan on the cover #BadOmen #MonthlyRed #TigerPussy #Vinyl #PinoyPunk #Filipino #Punk #LapuLapu #FilipinoPride #BattleOfMactan (at South of Heaven)
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Lasso Regression Analysis
import pandas as pd
import numpy as np
import os
import matplotlib.pylab as plt
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LassoLarsCV
from sklearn import preprocessing
emp_data = pd.read_csv('IBM HR Attrition Data.csv')
emp_data['Age'].describe()
emp_data.isna().sum()
emp_data.dtypes
df = emp_data[['Age','Education','Gender','OverTime','Attrition', 'HourlyRate','YearsInCurrentRole','DailyRate', 'DistanceFromHome','EmployeeCount','JobLevel','JobSatisfaction','EnvironmentSatisfaction', 'MonthlyRate','MonthlyIncome','NumCompaniesWorked','PerformanceRating', 'TotalWorkingYears','TrainingTimesLastYear','WorkLifeBalance','YearsInCurrentRole','TotalWorkingYears']]
df['Attri'] = np.where(df['Attrition'].str.contains("Y"),1,0)
df['Gender_cat'] = np.where(df['Gender'].str.contains("Female"),1,0)
df['OT_cat'] = np.where(df['OverTime'].str.contains("Y"),1,0)
predict = df[['Education','Attri','MonthlyIncome','Gender_cat','OT_cat', 'HourlyRate','YearsInCurrentRole','DailyRate', 'DistanceFromHome','EmployeeCount','JobLevel','JobSatisfaction','EnvironmentSatisfaction', 'MonthlyRate','PerformanceRating','NumCompaniesWorked', 'TotalWorkingYears','TrainingTimesLastYear','WorkLifeBalance','YearsInCurrentRole','TotalWorkingYears']] #'HourlyRate']]#,'Age','HourlyRate'
predictors = predict.copy()
predictors['Education'] = preprocessing.scale(predictors['Education']).astype('float64')
predictors['Attri'] = preprocessing.scale(predictors['Attri']).astype('float64')
predictors['MonthlyIncome'] = preprocessing.scale(predictors['MonthlyIncome']).astype('float64')
predictors['Gender_cat'] = preprocessing.scale(predictors['Gender_cat']).astype('float64')
predictors['OT_cat'] = preprocessing.scale(predictors['OT_cat']).astype('float64')
predictors['HourlyRate'] = preprocessing.scale(predictors['HourlyRate']).astype('float64')
predictors['YearsInCurrentRole'] = preprocessing.scale(predictors['YearsInCurrentRole']).astype('float64')
predictors['DailyRate'] = preprocessing.scale(predictors['DailyRate']).astype('float64')
predictors['DistanceFromHome'] = preprocessing.scale(predictors['DistanceFromHome']).astype('float64')
predictors['EmployeeCount'] = preprocessing.scale(predictors['EmployeeCount']).astype('float64')
predictors['JobLevel'] = preprocessing.scale(predictors['JobLevel']).astype('float64')
predictors['JobSatisfaction'] = preprocessing.scale(predictors['JobSatisfaction']).astype('float64')
predictors['EnvironmentSatisfaction'] = preprocessing.scale(predictors['EnvironmentSatisfaction']).astype('float64')
predictors['MonthlyRate'] = preprocessing.scale(predictors['MonthlyRate']).astype('float64')
predictors['PerformanceRating'] = preprocessing.scale(predictors['PerformanceRating']).astype('float64')
predictors['NumCompaniesWorked'] = preprocessing.scale(predictors['NumCompaniesWorked']).astype('float64')
predictors['TotalWorkingYears'] = preprocessing.scale(predictors['TotalWorkingYears']).astype('float64')
predictors['TrainingTimesLastYear'] = preprocessing.scale(predictors['TrainingTimesLastYear']).astype('float64')
predictors['WorkLifeBalance'] = preprocessing.scale(predictors['WorkLifeBalance']).astype('float64')
predictors['YearsInCurrentRole'] = preprocessing.scale(predictors['YearsInCurrentRole']).astype('float64')
predictors['TotalWorkingYears'] = preprocessing.scale(predictors['TotalWorkingYears']).astype('float64')
targets = df.Age #MonthlyIncome
predictors.head()
pred_train, pred_test, tar_train, tar_test = train_test_split(predictors, targets, test_size=.3)
print(pred_train.shape,pred_test.shape,tar_train.shape,tar_test.shape)
model = LassoLarsCV(cv=10,precompute=False,max_iter=15).fit(pred_train,tar_train)
pred = dict(zip(predictors.columns,model.coef_))
df2 = pd.DataFrame(pred.values(),index=pred.keys(),columns=['coef'])
df2['abs_coef'] = df2['coef'].apply(lambda x: abs(x))
df2.sort_values(by='abs_coef',ascending=False)
len(df2.index)
m_log_alphas = -np.log10(model.alphas_)
ax = plt.gca()
plt.plot(m_log_alphas, model.coef_path_.T)
plt.axvline(-np.log10(model.alpha_), linestyle='--', color='k',
label='alpha CV')
plt.ylabel('Regression Coefficients')
plt.xlabel('-log(alpha)')
plt.title('Regression Coefficients Progression for Lasso Paths')
m_log_alphascv = -np.log10(model.cv_alphas_)
plt.figure()
plt.plot(m_log_alphascv, model.mse_path_, ':') # plot mse with dotted line
plt.plot(m_log_alphascv, model.mse_path_.mean(axis=-1), 'k',
label='Average across the folds', linewidth=2)
plt.axvline(-np.log10(model.alpha_), linestyle='--', color='k',
label='alpha CV')
plt.legend()
plt.xlabel('-log(alpha)')
plt.ylabel('Mean squared error')
plt.title('Mean squared error on each fold')
train_error = mean_squared_error(tar_train, model.predict(pred_train))
test_error = mean_squared_error(tar_test, model.predict(pred_test))
print (f'training data MSE: {train_error}')
print (f'test data MSE: {test_error}')
# R-square from training and test data
rsquared_train = model.score(pred_train,tar_train)
rsquared_test = model.score(pred_test,tar_test)
print (f'training data R-square: {rsquared_train}')
print (f'test data R-square: {rsquared_test}')
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Why is it that the bigger the pain these cramps give me the better my stomach looks? Nothing about being a girl makes any sense. And nothing pisses me off as much as my own nature. Why can't I just be a hippie about it? 🔪🔪🔪🔪🔪#monthlyrant #excusezfuckingmoi Disclosure: I might delete this post later. ✌🏼
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Student loan tips from across the ditch has been published on Find and Select Business Reviews
New Post has been published on http://www.findandselect.com/tips/student-loan-tips-from-across-the-ditch.html
Student loan tips from across the ditch
It was definitely out of sight, out of mindpaying off my student loan.
I went travelling, didn't think about it, it popped in my heada few times – but I was like I'll worry about it when I get home.
When I first left New Zealand and took therepayment holiday, I didn't know about the free online transfer payments.
Being able to go on the myIR site and they'vegot the current balance of your loan, and how many repayments you've made – and seehow far I've got left to go.
Definitely get a myIR account, because thatway you can keep your details up to date and keep informed about how much you owe.
It was really good dealing with Inland Revenuewhen I called them.
They were really friendly, so helpful.
They didn't make me feel guiltyfor not paying any of my student loans.
I find it really useful that they're ableto contact me and I'm able to contact them so easily, because when I have a problem,especially when it's about money, it can be quite stressful.
And, I found that every time I'vehad an issue they've been able to deal with it really smoothly.
My advice in hindsight, would definitely beto pay off your student loan if you can in regular installments.
Just a little bit monthlyreally helps towards those end of year compulsory repayments.
When I've paid off my student loan, I'm goingto start saving for a house or another big trip.
It's going to feel really good.
Might go on a few extra surf trips, go upto Indonesia, enjoy myself a bit more you know.
I think there'll definitely be a celebrationonce I pay off my student loan.
And then I'll look forward to seeing that money go intomy savings.
Source: Youtube
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www.1stChoiceBHL.com
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You can find unbeatable motel deals under $800 near you. Stay comfortable without sacrificing quality on a budget. A month-long journey? Find the perfect home away from home. Here, https://www.truetravelplanner.com/800-a-month-motel/
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https://www.theprelease.com/property/syndicate-bank-2/
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Random Forest Attrition Analysis
from pandas import Series, DataFrame
import pandas as pd
import numpy as np
import os
import matplotlib.pylab as plt
from sklearn.model_selection import cross_validate
from sklearn.model_selection import train_test_split
#train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import classification_report
import sklearn.metrics
from sklearn import tree
from sklearn import datasets
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.ensemble import RandomForestClassifier
emp_data = pd.read_csv('IBM HR Attrition Data.csv')
emp_data.isna().sum()
emp_data.dtypes
emp_data['PerformanceRating'].describe()
df = emp_data[['Age','Education','Gender','OverTime', 'HourlyRate','YearsInCurrentRole','DailyRate', 'DistanceFromHome','EmployeeCount','JobLevel','JobSatisfaction','EnvironmentSatisfaction', 'MonthlyRate','MonthlyIncome','NumCompaniesWorked','PerformanceRating', 'TotalWorkingYears','TrainingTimesLastYear','WorkLifeBalance','YearsInCurrentRole','TotalWorkingYears']]
df['Gender_cat'] = np.where(df['Gender'].str.contains("Female"),1,0)
df['OT_cat'] = np.where(df['OverTime'].str.contains("Y"),1,0)
predictors = df[['Age','Education','Gender_cat','OT_cat', 'HourlyRate','YearsInCurrentRole','DailyRate', 'DistanceFromHome','EmployeeCount','JobLevel','JobSatisfaction','EnvironmentSatisfaction', 'MonthlyRate','MonthlyIncome','NumCompaniesWorked','PerformanceRating', 'TotalWorkingYears','TrainingTimesLastYear','WorkLifeBalance','YearsInCurrentRole','TotalWorkingYears']] #'HourlyRate']]#,'Age','HourlyRate'
targets = emp_data.Attrition
pred_train, pred_test, tar_train, tar_test = train_test_split(predictors, targets, test_size=.4)
print(pred_train.shape,pred_test.shape,tar_train.shape,tar_test.shape)
classifier = RandomForestClassifier(n_estimators=25)
classifier = classifier.fit(pred_train,tar_train)
predictions = classifier.predict(pred_test)
print(sklearn.metrics.confusion_matrix(tar_test,predictions))
sklearn.metrics.accuracy_score(tar_test,predictions)
model = ExtraTreesClassifier()
model.fit(pred_train,tar_train)
print(model.feature_importances_)
col_rank = {}
for col,rk in zip(predictors.columns,model.feature_importances_):
col_rank[col] = rk
pred = pd.DataFrame(col_rank.values(),index=col_rank.keys(),columns={'rank'})
pred.head()
pred.sort_values(by=['rank'],ascending=False)
trees=range(25)
accuracy=np.zeros(25)
for idx in range(len(trees)):
classifier = RandomForestClassifier(n_estimators=idx + 1)
classifier = classifier.fit(pred_train,tar_train)
predictions = classifier.predict(pred_test)
accuracy[idx] = sklearn.metrics.accuracy_score(tar_test, predictions)
plt.cla()
plt.plot(trees, accuracy);
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Smartphone Apps for House Searching
One of the best ways to search for your new home is through online. Before landing in Korea you can set a budget, decide on a neighborhood and even make some appointments to view properties of your choice. The ways people house search in Korea are in transition from person to person interaction (aka visiting a realtor) to few clicks on a website and even smartphone applications. This seems to reflect the trend in Korea as number of young, single or 2-person households is on the rise. Nevertheless it could make your house searching less stressful.
Note: Some basic Korean level will required for using below apps. These don’t provide multi-language service and google or other online translators will not translate every words.
Here are some apps for searching houses in Korea:
Zicbang (직방)
www.zicbang.com
Available for Android, IOS & Website
One of the first real estate apps launched in Korea it is known to have the largest number (5,000+) of realtors registered. You can search by the area or subway station, then filter the listing by deposit, monthly rent and number of rooms. You can view total number of listings from an interactive map.
Dabang (다방)
www.dabang.com
Available for Android, IOS & Website
The main difference between Dabang and Zicbang app is search options. Dabang filters the properties by the location and type but also by amenities such as parking lot, pets. Also it has private, direct listings(직거래) where property owners list their own.
I used both of the apps to search for my new home in Seoul recently. What I liked about using the app was that it helped me narrow down my choices. You can find the average rent cost between the different neighborhoods even before visiting a realtor. After deciding on few neighborhoods around my budget, I contacted the realtor to view the listing. This ‘pre-search’ was helpful since it set my expectation of the neighborhood.
The pictures on the app were somewhat accurate though it was difficult to imagine what the neighborhood actually looked like so I recommend using street view maps on naver or daum (google maps are not so accurate in Korea).
Overall, I think the apps are great for someone like myself who doesn’t know Seoul well enough to decide on certain areas. One of the downside of the apps were that by the time I made an appointment to view the house, it was taken off the market. Also, since many realtors tend to hold on to their private listing, I was able to view way more houses through a visit. It will be interesting to see how these online real estates evolve in the future. Meanwhile I recommend using these apps!
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